RCT #185 - Research Judgement Must Direct Research Resources. Plus: Good software engineer teammembers; Free tier as a business model; STRUDEL and UX; Python 3.13; Writing code for humans; Next level HPC support; Rackspace and OpenStack
Hi, all! Sorry for the pause in newsletter issues; I’m trying to figure out how to better work the newsletter writing into my weeks. You may see shorter round-up writeups for a while, which I think is going to be better-all around; thanks for your patience, and all feedback always welcome!
In #182, Scientific Judgement Is Our Job, I argued that applying scientific judgement to the work we do is just part of the job:
I keep hearing is that the team’s job is to support all research and researchers equally. That (a) is very much not what the team’s job is; (b) it wouldn’t be possible if it was […] It is not our teams’ jobs to just mindlessly churn out generic widgets for (or provide widget access to) anyone who asks. We are hired for our judgement. In fact, we’re hired explicitly for our (rare! precious!) combinations of scientific and technical judgement. We apply that judgement in make decisions about who to support, how much effort to put into each effort, and what to go after next.
I got some good push-back on how I stated this, so I want to add some nuance to this and expand it a bit.
It’s worth spending more time on because it’s pretty fundamental to the main tenets of this newsletter: that our job is to advance research and scholarship as far as we can, given the constraints we face; and that this is an important, challenging, and rewarding job, worth doing with professionalism.
Research resources — funding, equipment time, lab space, the professional time of technical experts like those of our teams who support research work — are far too scarce and precious to just assign them “first come, first serve”.
There’s a reason why grant funding requires so much justification and is so competitive, why telescopes or other major scientific facilities have allocation committees, why time on major supercomputers is competitively assigned. There’s too much work to be done, and worryingly few resources to do it all.
So as a community, research has as a foundational principle that research judgement must direct those scarce research resources to where they can do the most good.
Like anything involving human judgement and predictions of future outcomes, it’s a messy, flawed process. Disappointingly, there’s been a lot of recent work showing that these processes are still far too inequitable in a number of predictable ways, both in who the funding goes to and in whose problems are being researched.
But “just give the resources to whoever asks for them” isn’t an alternative, particularly since the loudest voices and most assertive requests tend to be disproportionately from groups that are doing pretty darn well under the current allocation of resources.
So judgement has to play a role. Does it have to be ours, though? We just want to support research; making these kinds of judgement calls seems like (is!) an enormous responsibility to take on.
Well, it has to be someone’s. There’s a few ways it can happen.
One is that funding goes straight to the researcher, who then decides how to spend that funding — maybe with our team, maybe on a postdoc salary or piece of equipment for their lab. I’m a BIG fan of this model. Here, there’s a couple layers of research judgement being applied; some funding agency ranked the proposed project as one of the (too few) to receive funding, and then the researcher themselves decided that in the execution of the project, the most cost-effective way forward were the services we offered which were worth paying for (#178). In this case, we don’t need to really decide “is this effort is worth doing”; that question has been asked and answered a couple of times. Here we just need to make use of our scientific and technical expertise to execute the project well and effectively.
This approach has much to commend it. For one it means that teams that are focussed on the needs of their research community can thrive, and it can enable the existence of really specialized facilities. This is one of the reasons that this is approach is the default in biomedical cores.
There are some real downsides people point out in objections. One is that in many jurisdictions, the rules by which teams are allowed to operate under this model within a university are extremely onerous and restrictive.
Another is that not all resources (but more than you might think!) can be funded through a number of individual researcher purchasing decisions; some need some sort of collective action.
A third is that it depends on a fairly broken and biased research funding model. That’s a real problem, but one best addressed at the root (get on funding committees; make sure research papers about problem with the current funding system get widely read and understood) rather than everyone trying to do ad-hoc workarounds individually.
On the other hand, an objection which isn’t at all compelling is, “but researchers would never pay their own money for these services”. If it’s true that researchers in the community would never see the services as valuable enough to spend their own research funding on those services, what exactly is the justification for allocating some other pool of all-to-scarce research funding on providing them? And why would anyone want to spend a good part of their career offering services that researchers wouldn’t bother to pay for if they had to?
So that’s one proven and successful model that works in a number of contexts.
A second model is that funding goes to some centre or group which has resources, and there’s a competitive allocation process for allocating those resources. Here, the allocation committee is playing the role of a funding agency, “funding” the research project with access to expertise or equipment.
(I’ve had a few people in a few different contexts take issue with this comparison between the allocation committee and a funding agency, which baffles me. To my mind this isn’t controversial at all. It’s allocating funding, but the funding is in the form of basically gift cards you can only use at this facility - pre-paid credits for compute or software services or data science work or bioinformatics services or the like.)
Again, the research judgement directing research resources question is asked and answered by the allocation committee; we can accept that decision and simply apply our judgement to doing the work.
This is a perfectly fine and time-tested approach. It works best with large, one-of-a-kind resources, like the large telescopes or other major scientific equipment facilities, which is where this method began.
But I find it’s applied more widely than is really optimal.
- There’s the fundamental issue that these committees are separate, and in particular usually disconnected from grant funding, so you can have researchers in the nonsensical position of one committee having given them funding to hire a postdoc to do a project, but another not given them them the compute time/software support/data support necessary to do it, or vice versa
- Relatedly, the most interesting research projects these days often require more than one type of technical research resource - data support and software development, or software development and computing time - and these committees are almost always siloed resource-by-resource, meaning they might have the (say) compute time but not the software support needed to do the work effectively.
- The resources being allocated often are local quasi-monopsonies — the only “seller” of such resources available in the institution or community — even though analogous resources with different capabilities are available elsewhere. (and remember, those other capabilities are colleagues, not competitors - #142) Even though one of those other resources might be a better match for the research in question, these “allocation gift cards” are all that is offered and taking them elsewhere isn’t an option. Having these allocations happen at the national or regional level, aggregated across multiple resources, helps a great deal.
Finally, there’s the model where there’s no such superstructure deciding which and how much research resources to apply where, and so it comes down to us.
The default situation in this case is just to avoid making such decisions, and passively be order-takers, sitting in our offices, waiting for requests, and doing whatever anyone asks us to do. To be clear, this model can work when (say) a team is just starting out and isn’t yet sure where it can have real impact.
But as demand and experience grows, this turns into my least favourite model where there is no such research judgement of what to do being applied in any consistent way - there’s no discussion of where and how to best apply effort, what kind of activities we’re doing matter, what the impact of anything is; so everyone on the team just sorts of plays it by ear, which typically just means doing what they’ve always done instead of focussing on the highest bang-for-buck work (#177), and continuing providing services for the same people indefinitely or those who ask for it most loudly.
I see this fairly often when the headline resource is access to equipment like computing time, and on top of that there’s also a lot of people time available for support. Here the hardware is almost allocated through a formal committee. On the other hand, how much people’s time, expertise and support should be offered, and to who, is an afterthought. (The centres who do this almost always have text on some webpage or another saying “people are our most important resource”, buried in the middle of 50 pages of talking about the computers).
However it comes to be, this ad-hoc order-taking model isn’t effective. It’s not a responsible stewarding of the research resources we’ve been given. It’s not taking seriously the charge our community has given to us, to advance research and scholarship within our community as best we can given the constraints we face. It’s abdicating our responsibility to our staff, who have the right to see their efforts consistently have the highest impact possible.
I also find it a bit baffling. The inevitable justification for having such services delivered locally is that “the local team understands the institution and knows the research group”. What’s the point of a team having such rich local context and knowledge if it’s not to inform decisions?
So as a community I’d like us to move away from this model (lack of a model?) and towards something more systematic and active. When we do find ourselves in this situation, I’d like to see more of our teams move away from a passive, “take a number and we’ll be right with you” approach towards taking an active role in pursuing high-bang-for-buck projects and reducing our time spent on lower-impact work. Where we are starting to have intuition of how we can have impact, we can start sketching crude logic models (#163), thinking in terms of positioning, prioritization, planning, and stakeholder engagement (#168), and generating success stories (#179). That’s how we get the research support flywheel going (#176).
And with that, on to the roundup!
Managing Teams
On the other side of the internet, over at Manager, Ph.D., in the current issue I talked about wrestling with ambiguity when in our early scientific training we were really only taught to deal with uncertainty.
Round-up articles were on:
- Feedback is for behaviour change, not self-expression
- Feedback on subjective topics (hint: almost all feedback is subjective)
- Trust is necessary to for us to know what’s going on
- Change is risky, and makes things worse before improving them
- If people aren’t reading our emails, that’s on us.
Technical Leadership
On Good Software Engineers - Candost Dagdeviren
Dagdeviren gives a quick definition of what he looks for and tries to develop in technical staff
A good engineer is one whom I, as a manager or peer, can trust to progress a project, knowing that they will deliver a solution by working with the team and producing good quality, again and again.
and gives some examples of what that looks like (in much more detail than I’ve summarized below)
- know how to influence others and the organization to deliver a solution as a team
- understand the processes they are operating in while taking a project from idea to solution
- spend extra time learning the environment they’re in so they can independently drive the work
- take a proactive approach and embed quality elements into their deliverables to improve consistency and velocity
- understand the stakeholder’s need and fine-tune their approach accordingly
- constantly work on reducing complexity
- are reliable in consistently changing organizations
- are team players regardless of personality type
The argument is that this can be applied at all levels of seniority.
I really like this, and I think these sorts of skills are what we should be aiming to develop in our staff. The scope for each expands as you go up in seniority, but the basic ideas - delivering as a team, and creating the environment where the team can deliver - is what we’re aiming for.
In our line of work, we and our staff have grown up in environments where the focus tends to be on individual smarts, individual expertise, and individual performance. But in our teams, we’re delivering together (and often with other teams), and someone toiling brilliantly alone at their desk is only doing part of the job.
Reaffirming our commitment to free - Nitin Rao, Liam Reese, James Allworth, Cloudflare blog
This article, like Tailscale’s article on “How our free plan stays free”, very clearly outlines where the free tiers fit into the business model of their respective companies, and reiterates their ongoing support for those free tiers. (Disclaimer - I happily use free tiers of both Cloudflare for this newsletter and Tailscale just for messing around with my various computers, but don’t have any other relationship of any kind with either of them).
I wrote in #170 about risk management and gratis offerings. In this post-ZIRP environment (roughly 2008-2023), tech companies are massively cutting on back spending, cutting back hiring (and having massive layoffs), and focussing on profitability rather than growth and goodwill. That means free tier offerings are dropping left and right, and we should be very careful about taking free tiers of anything and having them be key parts of our delivery mechanism for services.
That doesn’t mean we should steer well clear of all such offerings - how could we? It just means being careful and using basic risk management approaches.
Part of that caution extends to understanding roughly the business model of the company offering the free thing, and how it plays into their operations. In both of these cases, the authors explain why free tier users are of very little extra marginal cost to them, while providing real value not just in terms of goodwill but as key parts of their go-to-market and customer-feedback-and-improvement activities.
Being able to think a little bit like a business person and understand cost-benefit tradeoffs is useful not internally for for our own operations (after all, we’re professional services firms of a sort, #127) but in dealing externally with vendors.
2M users but no money in the bank. Tough times - Jeremy Walker
Maybe relatedly, and certainly soberingly, the partial wind-down in September of Exercism (a programming tutorial community) reminds rather forcefully how hard it is to be in our kind of operation, professional technical services delivery operations run in a nonprofit business model (#164). Providing a general good where everyone benefits somewhat but no individual community benefits enough to be willing to pay for it is just a savage place to find yourself when money starts getting tight:
I've lost faith in the nonprofit business model working in a way that allows Exercism to reach any of its potential. Keeping something free for everyone relies on either the user being the product, or on significant donations, and without either, it's very hard to grow.
Product Management and Working with Research Communities
The Journey to STRUDEL: How We Came to Embrace User Experience in Scientific Ecosystems - Lavanya Ramakrishan
Ramakrishan gives a good talk about an effort towards understanding what UX means in our ecosystem, and the STRUDEL project provides some basic frameworks others can use towards thinking about and improving user experience.
This is important and worth thinking about. Researcher’s time matters, too (#102). If we want to power a research flywheel (#176), we want as little friction as possible in scientific workflows.
And since interesting research projects increasingly cross data/software/systems practice boundaries, we don’t want to break up into digital research practice silos, even if we specialize (#114), so user research and user experience has be considered across the entire user journey from data and software to systems and job or deployment.
Research Software Development
Everything you need to know about Python 3.13 – JIT and GIL went up the hill - Drew Silcock
Good overview about the optional free-threading build and first pass at JIT that’s part of October’s CPython 3.13 release.
It's hard to write code for computers, but it's even harder to write code for humans - Erik Bernhardsson
I was asked in a job interview a zillion years ago what I thought the most important advance in programming was over the past decade or so. I answered, without hesitation, the fact that the software engineering community was taking the idea of cognitive load seriously - that writing code for humans was at least as important for writing it for compilers.
“Writing code for humans” is the one of ideas that is even more important in scientific software development than in (say) tech. Our codes encode a lot of subtlety, even more than complexity, and they can live and be used for decades.
Bernhardsson gives some advice about writing code that’s going to be used and modified by humans, and become part of a workflow used by teams of humans:
- Examples, not “core concepts”
- Good error messages
- Avoid conceptual overload
- Use familiar names for things
- Make it programmable (“People will do crazy things with your codebase” - truer for scientific code than anywhere else, I think. Programmability, I think, is even more important in this world where our users are using GitHub copilot or other LLM tools for coding - #173)
- Be careful about “magic”
Research Computing Systems
INESC: European supercomputer users can now get specialized “next level” support and training - Science Business European supercomputer users can now get specialized “next level” support and training - CSC Blog
Between this effort and efforts by XSEDE and (to a lesser extent) ACCESS, I’m pleased that high-level, more professional-services like support offerings are starting to be a model more familiar to the research community. I do worry about there being a “missing middle” between helpdesk-type support and significant support engagements, but still having the bookends in place is terrific.
Rackspace Goes All In - Again - On OpenStack - Jeffrey Burt, Next Platform
This is good news. The community needs something like OpenStack (and yes, in which case it also needs OpenStack Enterprise as a revenue model), so it’s great to hear that Rackspace is recommitting to its development.
Random
DRAMHiT is a very cool high-performance hash table from the Mars research group @ U Utah, which uses distributed systems approaches within a single modern multi-core, multi-NUMA domain node because inter-core communications is (according to the authors) dominant for hash-table workloads. Very interesting technically (paper is linked) and the approach taken.
Google internally recommends memory-safe languages for new system software, but does not recommend wholesale rewriting of old code into such languages because new code, in any language, has vastly more bugs.
A great history of block storage at AWS.
Has anyone used automated tools like creduce to produce compiler bug reproducers?
I would love for Python adopt a reactive notebook like marimo, the way Julia has with Pluto. But then again, I’d kind of prefer something like Posit studio which has great off-ramps to get code into files and version control to get super popular. Well, guess we’ll see what happens.
In praise of tidying up code as you go along, in small bits, as long as it works out.
Very cool looking course on quantum machine learning, out of École de Techologie Supérieure
As long-time readers will know, this newsletter is principally an embedded database fanzine; thus, it pleases me to no end that you can run entire Rails 8 apps now in production w/ SQLite, powered somewhat by the community’s realization that SQLite is fine for such workloads if you choose defaults accordingly.
Are weblinks finally coming back? Can’t wait to look up how the newsletter website is doing on technorati!
Something I’d like to write about once I’ve had more time to think it through is summed up in this very brief article (and really, just the headline): AWS executive says regulated industries moving fastest on AI. One of the weird things about ML-type adoption (like cloud, really) is that it’s hitting fastest in areas we think of as being pretty staid, technology wise - finance, health, qualitative research, even pure mathematics. I think that’s caught our teams kind of off guard.
You get quadruple precision! You get quadruple precision! Everyone gets quadruple precision!!
That’s it…
And that’s it for another issue. If any of the above was interesting or helpful, feel free to share it wherever you think it’d be useful! And let me know what you thought, or if you have anything you’d like to share about the newsletter or stewarding and leading our teams. Just email me, or reply to this newsletter if you get it in your inbox.
Have a great weekend, and good luck in the coming weeks with your research computing team,
Jonathan
About This Newsletter
Research computing - the intertwined streams of software development, systems, data management and analysis - is much more than technology. It’s teams, it’s communities, it’s product management - it’s people. It’s also one of the most important ways we can be supporting science, scholarship, and R&D today.
So research computing teams are too important to research to be managed poorly. But no one teaches us how to be effective managers and leaders in academia. We have an advantage, though - working in research collaborations have taught us the advanced management skills, but not the basics.
This newsletter focusses on providing new and experienced research computing and data managers the tools they need to be good managers without the stress, and to help their teams achieve great results and grow their careers. All original material shared in this newsletter is licensed under CC BY-NC 4.0. Others’ material referred to in the newsletter are copyright the respective owners.
Jobs Leading Research Computing Teams
This week’s new-listing highlights are below in the email edition; the full listing of 336 jobs is, as ever, available on the job board.
Quantum Education Program Manager - University of Colorado Boulder, Boulder CO USA
The QSEnSE NSF Quantum Leap Challenge Institute at University of Colorado Boulder invites applications for a half-time Quantum Education Program Manager. This position will report to the QSEnSE Director for Education and Workforce Development and will be responsible for supporting and managing QSEnSE’s major EWD initiatives, including through content/curriculum creation, maintenance of relationships with community stakeholders, and management of ongoing programs and program participants.
Quantum Information Lead, Optimization and Machine Learning - Deloitte, Chicago IL USA
As the Quantum Information Specialist, you will work with the US Quantum team to drive the development of Deloitte's quantum computing solutions and capabilities. You will support strategic discussions to help solve client business challenges and drive their competitive advantage. You will help shape Deloitte's ecosystem, capabilities, and solutions for Quantum Computing technologies across all industries and clients we serve.
Scientific Project Manager - New York Genome Centre, New York NY USA
The Scientific Project Manager provides operational project management and scientific collaboration support for NYGC across the full range of sequencing operations projects. This includes day-to-day management of scientific projects and collaborations, involvement in the consultation process with potential investigators, and assisting senior managers with daily scientific tasks or projects. In addition, the Scientific Project Manager is responsible for tracking inquiries, projects, action items, deadlines, and organizing all supporting information.
Research Computing Manager - Stanford, Stanford CA USA
Stanford Research Computing is looking for a manager to join our team of collaborative and innovative professionals assisting Stanford’s faculty and students to use high-performance computing and data tools to explore new frontiers in knowledge and solve some of humanity’s most urgent problems.
This position is a part of the management team within Stanford’s Research Computing organization, leading a group of staff who work directly with Stanford researchers. This includes development and delivery of training and workshops, onboarding activities for new researchers, and assistance on a broad variety of topics via direct consultation, office hours and tickets. Topics can range from the basics of cluster and cloud computing to data movement and storage to assistance with specialized software.
Lab Manager, Center for Biodiversity Genomics - University of Guelph, Guelph ON CA
The Laboratory Manager will play a critical role in driving the success of our research programs by ensuring the smooth operation of the laboratory and supporting cutting-edge genomic projects. The successful candidate will oversee daily lab operations, ensure the quality and efficiency of experimental workflows, and maintain the highest standards of safety and compliance. Day-to-day operational oversight involves requisition of reagents and consumables, inventory management, instrument maintenance and repair. The Laboratory Manager is also responsible for implementation and enforcement of operational policies and procedures for the facility, training of staff, and ensuring technical staff meet set production quotas. The Laboratory Manager also contributes to research and development, continuous improvement initiatives, client support, and coordinates project-based activities as required by the Director of CCDB.
Project Manager, Brain Health AI-deas Hub - University of Cambridge, Cambridge UK
The BrainHealth AI-deas Hub (https://www.abg.psychol.cam.ac.uk/brainhealth-ai-deas-hub/vision) is a cross-disciplinary programme that is funded by AI@Cam (https://ai.cam.ac.uk/ai-deas/) and works at the interface of Artificial Intelligence, Neuroscience and Clinical practice to advance the development and implementation of tools for early prediction of brain and mental health disorders and aid their translation to real-world clinical settings. By supporting cross-disciplinary projects, the Hub will seek to promote AI for lifelong better brain and mental health with impact in clinical practice, industry and policy. The Project Manager will coordinate the work of the Brain Health AI-deas Hub, external partners across sectors (academia, charities, industry, government), related research and translational initiatives, funders and policy makers, managing the Initiative's day-to-day activities and working closely with the Directors on the Hub's future development.
Senior Research Software Engineer and Manager - University of Birmingham, Birmingham UK
ARC builds and runs (or buys-in) a range of specialist services for researchers, collectively known as BEAR (Birmingham Environment for Academic Research). Aimed at all disciplines, BEAR covers an increasingly broad spectrum of needs, ranging from the traditional HPC through storage and archiving solutions to collaboration and analytics tools and new ‘on premises’ cloud HTC offerings. ARC also supports the exploitation of specialist regional and national services, including HPC Midlands Plus and the new Birmingham-based Baskerville supercomputer. Together with the national supercomputer Archer (Tier 1) and Birmingham’s own BEAR infrastructure, they form a powerful resource for research.
Lead Software Engineer - Data - Alice & Bob, Paris FR
As a Lead Software Engineer - Data at Alice&Bob, you will be responsible for building (which currently doesn’t exist) and leading the Software Data Team, to drive the implementation of the company's data initiatives. Superconducting quantum chips can generate vast amount of data in mere milliseconds. Alice & Bob recruits a lead data engineer to lead and manage this critical data management. Your mission is to create this team, and to develop and execute a data strategy that aligns with business objectives while guiding and mentoring your team to achieve peak performance.
Bioinformatics Core Manager/Platform Lead - University of Manitoba, Winnipeg MB CA
Meets researchers to discuss and understand their projects requested for consultation. Performs study design and sample size calculation, and assists researchers in grant proposal writing. Performs quality control of raw omic data, data management, builds customized analysis pipelines, performs data analysis, result interpretation, and writes summaries of data analysis. Performs bioinformatics analysis for approved projects on a fee-for-service basis. Coordinates, prepares, and teaches workshops on selected topics to basic and clinical researchers. The workshops will be held 2-4 times a year on 2-4 different topics. Each workshop will be 3 hours with a 1.5-hour theory and 1.5-hour practice sections. Example topics may include applied genomic medicine, bulk/single-cell RNA sequencing data analysis, omics data visualization, pathway and network analysis, R and Python programming for bioinformatics, artificial intelligence for bioinformatics, and training for common bioinformatic tools (e.g., Ingenuity Pathway Analysis).
Director, AI Co-Innovation Labs - Microsoft, Redmond WA USA
We are seeking a Director, AI Co-Innovation Labs to join and lead the Business Development Strategy & Ventures (BDSV) AI Co-Innovation Laboratories team to support partners and customers through their hardware, software, and AI challenges, via rapid ideation and prototyping engagements, on their path to market. The lab programs are a critical asset that enriches and accelerates strategic partnerships across Microsoft.
IT Technical Lead for Quantum Computing and HPC Services - University of Missouri, Columbia MS USA
The University of Missouri is advancing research capabilities in the state, embarking on a new, vendor-partnered, quantum computing initiative. The Technical Lead for Quantum Computing and HPC Services will be responsible for overseeing the development, implementation, and maintenance of quantum computing and high-performance computing services within the academic institution. The technical lead will: Lead the teams that manage the deployment, operation, and integration of vended quantum computing systems and on-premises HPC clusters, ensuring high availability and performance. Oversee personnel engaged in research facilitation and direct user support for advanced systems. Collaborate with faculty, researchers, and IT staff to identify needs and develop solutions that enhance research capabilities.
Director of the AI Hub - University of Wisconsin, Madison WI USA
The Wisconsin School of Business (WSB) is seeking its first staff Director to lead the Artificial Intelligence (AI) hub. The purpose of the AI hub at the Wisconsin School of Business is to serve as the focal point for all AI related activities at the WSB. It serves four primary functions that relate to AI: support research, nurture industry connections, host industry and academic events, and support AI related communications at the WSB. In this leadership role, the hub director will oversee these four primary functions. In addition, the hub director will teach 6-9 credit-hours of AI related coursework in our undergraduate or graduate business programs. The hub director will report to the faculty director for the AI hub and will work in close collaboration with internal (e.g., students, staff, faculty) and external stakeholders (e.g., industry, alumni) of the WSB. The ideal candidate should have a passion for AI with industry experience, a strong desire to continue to learn and teach AI, and outstanding organizational, interpersonal, and communication skills. While experience in an academic setting is not required, it is an advantage. We seek a self-motivated, collaborative leader with a proven background in AI
Director, Advanced Light Microscopy Core - University of Missouri, Columbia MS USA
The University of Missouri is pleased to announce an opening for a Non-Tenure Track Assistant Research Professor position with a working title of Director – Thomas E. Phillips Light Microscopy Facility. This position directs the daily operations and strategic planning of the Thomas E. Phillips Light Microscopy Facility, an advanced light microscopy core (ALMC), a shared campus resource. The core is an invaluable resource for light microscopy, immunocytochemistry and general scientific image analysis and processing. The ALMC’s equipment and services consist of confocal, super-resolution, digital light-sheet and widefield microscopes, laser capture microdissection, image analysis and processing and sample preparation. The Core also boasts an onsite supply center stocked with fluorescent secondary antibodies and markers. Consumables for laser capture microdissection are also available.
Cryo-Electron Microscopy Facility Manager - Virginia Commonwealth University, Richmond VA USA
A Facility Manager position is available for the new Cryo-Electron Microscopy at VCU. The Facility has a ThermoFisher Tundra Cryo-EM instrument and ancillary equipment. The Manager will report to the Faculty Director.
Responsible AI Risk Manager - MetLife, New York NY USA
The Responsible AI Risk Manager will oversee the day-to-day operations related to AI risk management and ensure the completeness of AI risk mitigation processes within our organization. This position will focus on implementing and maintaining responsible AI practices in our operational workflows across MetLife
Artificial Intelligence (AI) Accelerator Lead Technical Program Manager (TPM) - Microsoft, Various USA
You will collaborate deeply with leaders across the Commercial Solution Areas to prioritize and build AI (Artificial Intelligence) solutions grounded in consistent architectures that have proven repeatability with customers. These solutions will mature our sales motions in the field. You will work with AI engineering product groups to advocate for customers and partners in relevant product strategy. Similarly, you will work closely with AI marketing, sales excellence teams, and customer success + delivery organizations to integrate gold standard assets and methodologies into existing go-to-market motions and prioritization of key AI use cases that drive differentiated business outcomes.
Manager, Informatics - University of Nebraska-Lincoln, Lincoln NE USA
Collaborate on informatics and data analytics projects, providing engineering expertise in design, layout, and implementation. Participate in project design meetings with internal and external stakeholders. Develop, update, and validate analytical models, predictions, and reports.
Software Development Manager, High Performance Computing - AWS, Boston MA USA
You will be leading a team in Boston and be part of an engineering org that is based in Boston, Seattle, and Italy. The ideal candidate will have strong distributed systems design and software engineering experience, Linux/Unix and networking fundamentals, and a passion for AWS technology. In this role you will be responsible for leading engineers in tackling core software engineering problems - distributed computing, resource usage efficiency, and rock solid testing. We also work with a lot of AWS cloud technologies (e.g., EC2, S3, ECS, Lambda, FSx for Lustre, Batch, Lambda, Elastic Fabric Adapter- EFA) to design and run highly scalable systems.
Senior Project Manager, European Genomic Data Infrastructure - EMBL-EBI, Hinxton UK
As a member of the ELIXIR Project Management Office, you will support the PIs and Scientific Leads under the supervision of the Ho PMO. You will work closely with the PMO Manager, ELIXIR PIs, Project Managers, Technical Coordinators, External Relations and the Operations team, and colleagues at the 24 national ELIXIR Nodes and other project partners throughout Europe as well as the European Commission officers monitoring the implementation of the project.
Manager, LLM Accuracy Evaluation - NVIDIA, Various Europe, Remote
We are looking for a visionary Manager to lead a team of extraordinary engineers in pioneering new methodologies for evaluating the performance of pioneering deep learning models, including LLMs, RAG, agents, and vision models. As a manager, you will play a critical role in riving the development and deployment of the latest flagship models from our community and partners—such as Gemma and Llama-3—as optimized NVIDIA Inference Microservices (NIM). This leadership position oNers an outstanding opportunity to craft the future of AI at a rapidly growing company at the forefront of the AI revolution. You will guide our team to deliver the most sophisticated models with lightning-fast inference, working on the most powerful enterprise-grade GPU clusters and gaining early access to unreleased hardware. Your leadership will directly influence NVIDIA's roadmap and the broader AI landscape, making a lasting impact on the industry!
AI Research Lead - MI5, London or Manchester UK
As an AI Research Lead, you’ll conduct research into issues MI5 could face and consider how we can overcome these risks. You’ll join a team of Research Leads and work alongside Principal Scientists, Project Managers, and Subject Matter Experts to agree on and deliver a scientific research programme that is fit for purpose and supports the needs of future users.
Quantum Technology Research Manager, - University of New Mexico, Albuquerque NM USA
The Center for High Technology Materials (CHTM) at the University of New Mexico (UNM) is searching for a Quantum Technology Research Manager who is passionate about developing research training programs in a university environment. The Quantum Technology Research Manager will primarily oversee and manage two academic research training programs at UNM: the Quantum Photonics and Quantum Technology graduate program (QPAQT) and the Quantum Undergraduate Research Experience at CHTM (QU-REACH). Specific duties include: organizing faculty and collaborators meetings, serving as liaison for program evaluation and external partnerships with companies and national labs, reporting to funding agencies, recruiting and advising students (including managing applications and admissions), maintaining websites and overseeing day-to-day program communications, and organizing a weekly seminar. The individual may also be expected to develop modules for the weekly seminar on topics such as science communication.
Director of Core Facilities - University of Massachusetts, Amherst, Amherst MA USA
The Director of Core Facilities provides strategic, administrative, fiscal, and sustainability oversight and leadership for all current and future centralized Core Facilities at UMass Amherst, housed under the Institute for Applied Life Sciences. “Core Facility” refers to a multi-user research facility supporting UMass faculty and students, faculty from other academic institutions, as well as external industry/medical center users.
Core Research Lab Manager - Beth Israel Deaconess Medical Center, Boston MA USA
Oversees Core operations on a daily basis and will meet with investigators and core customers at Beth Israel Deaconess Medical Center (BIDMC), and other research institutions. May delegate work to other research staff within the Core and collaborates with research investigators in the design and analysis of experiments requiring this technology. Mentors junior level research assistants within the Core Facility.
Research Infrastructure Manager - University of Nevada, Las Vegas, Las Vegas NV USA
Direct the operations of three research facilities supporting multiple and varied research programs across colleges and departments campus-wide including HRC, CLB3 and ALB; collaborate on other VPR facilities. Manage comprehensive facility technical, operational, and MEP functions, support, and maintenance. Provide direct and indirect support and guidance to Principal Investigators, researchers, and technical staff using the facilities. Proactively identify existing and potential problems, investigate, and find creative solutions.
Research Infrastructure Core Manager, Center for Biomedical Resarch - Tuskegee University, Tuskegee AL USA
Tuskegee University Center for Biomedical Research (CBR) is seeking multi-talented applicants for its Research Infrastructure Core Manager position. The Manager is committed to ensuring that our research infrastructure caters to user needs, aligns with cutting-edge technologies, and functions optimally and proficiently. This multi-faceted position demands exceptional organizational skills, attention to detail, and the ability to manage a diverse range of responsibilities.
Director, Departmental Computing, Institute for Protein Design - University of Washington, Seattle WA USA
Located at the University of Washington in Seattle, the Institute for Protein Design is a unique interdisciplinary environment where world-class researchers create new biomolecules. Our mission is to create proteins that solve modern challenges in medicine, technology, and sustainability by leveraging computational and AI tools for protein design. As such the computational infrastructure of the institute is critical to the overall success of the IPD. The Director of Computing at the Institute for Protein Design (IPD) is a key leadership role within the institute and will manage a team of Professional Staff in IT that will oversee the smooth running of the IPD’s high performance compute infrastructure. They will have responsibility for planning and managing the computing infrastructure, ensuring cyber security, developing internal software tools for the institute, and ensuring that they remain at the cutting edge.
Director, Clinical Research Computing Unit - University of Pennsylvania, Philadelphia PA USA
The Center for Clinical Epidemiology and Biostatistics at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for an Associate or Full Professor position in either the non-tenure clinician educator track or the tenure track. Applicants must have an M.D and/or Ph.D. or equivalent degree. Expertise is required in the specific area of research leadership and team science, including management of staff. The desired candidate will supervise two directors and three associate directors; monitor the FTE load well enough to know over the course of a year the risk and benefits of hiring; envision the work of the CRCU three years ahead to monitor effort against funding in hand vs. funding pending, as well as assist in creating budgets, prepare materials for presentations about the CRCU, assist with marketing of the CRCU across the university and cultivate a relationship with leadership in clinical research across the Perelman School of Medicine.
Program Manager, AI & Society - CIFAR, Toronto ON CA
Reporting to the Director, Pan-Canadian AI Strategy, the Program Manager, AI & Society, will lead the management of the AI & Society program and its related activities, as part of the Pan-Canadian AI Strategy. Provide institutional support and guidance to CIFAR Solution Networks and their related activities. Support and maintain relationships with network co-directors, and network members through attending meetings and other formal and informal network meetings. Work with the Director, Pan-Canadian AI Strategy and Network leadership to develop and implement network engagement meetings and activities
Director, Bioinformatics & Analytics Core - University of Missouri, Columbia MS USA
The University of Missouri (MU) is pleased to announce an opening for a Non-Tenure Track Assistant Research Professor position with a working title of Director – Bioinformatics and Analytics Core. This position directs the daily operations and strategic planning of the Bioinformatics and Analytics Core (BAC), a shared campus resource. BAC is a centralized resource for providing expert bioinformatics, analytical and data science consulting and analysis solutions for the UM System as well as public and private industry partners. The core offers services to investigators within and outside MU on grant-funded and fee-for-service projects for the management and analysis of large-scale biological datasets produced in a variety of ways.
Manager Computer Operations - University of Mississippi, University MS USA
This position manages and coordinates the activities of the Computer Operations section of the University Computer Center and the Mississippi Center for Supercomputing Research to ensure that computing resources are provided, data is secure, resources are available statewide and worldwide, and production schedules and quality specifications are met. Supervision is exercised over employees in lower classifications.
Director of Research Computing and Data - University of Utah, Salt Lake City UT USA
The Director of Research Computing and Data will assume a pivotal role at the University of Utah. This position will lead the strategic direction in research computing and data to support and advance the University’s pioneering computational and data-enabled research (both open and protected). The Director of Research Computing and Data will provide visionary leadership, experience, strategic direction, and execution guidance to shape the future research computing and data cyberinfrastructure across the University of Utah. They will provide resource and budget planning, faculty support, and expertise in the research computing and data. The Director of Research Computing and Data will be a strategic thinker with an open and collaborative style who fosters teamwork, sustains a learning environment for growing number of staff and develops an open, engaged culture that engenders trust from the broader university-wide research community.
National Data Project Manager, Hearts in Rhythm Organization - University of British Columbia, Vancouver BC CA
The HiRO Research UBC/Vancouver team provides the research infrastructure for both the local site with related enrolment, and the National HiRO Registry as the Coordinating Center. The Data Project Manager reports directly to Dr. Andrew Krahn, and will work closely with National HiRO regulatory coordinator, finance and biobank coordinator, and both local and national HiRO site coordinators.
Data Science Program Manager - Imperial College of London, London UK
We are seeking a Program Manager to support a newly appointed BHF Chair of Cardiovascular AI and the operation of a Cardiovascular Data Science Hub. You will be working with Prof O’Regan and his team to support a program of research using cardiovascular imaging, genetics and health data to discover disease mechanisms and predict outcomes. You will work within a diverse multidisciplinary team of computer scientists, geneticists and clinicians exploring large and complex datasets. You will have a key role in the team managing data science resources and analysis pipelines, providing expert support to researchers, and liaising with our research partners.
Cryo-EM Manager - University of Minnesota, Minneapolis MN USA
The University of Minnesota is seeking an experienced cryo-transmission electron microscopist in the Department of Biochemistry, Molecular Biology and Biophysics who will work in the Characterization Facility (CharFac), which houses a variety of transmission and scanning electron microscopes in addition to other biological and advanced materials characterization instruments. New acquisitions include a cryo-electron transmission microscope (cryo-TEM; Glacios 2) and a cryo-focused ion beam-scanning electron microscope (cryo-FIB-SEM; Aquilos 2). The successful candidate must be able to support a variety of cryo-TEM-related activities, both independently and in collaboration with users ranging from those seeking the services of an expert microscopist (i.e., researchers with minimal cryo-TEM understanding) to those seeking training to become independent cryo-electron microscopists. The job includes substantial TEM management (instrumentation and user base) as well as training aspects, as the CharFac has a long history of training and mentoring students and postdocs to use state-of-the-art materials characterization instrumentation. The successful applicant will have opportunities beyond their support duties to pursue their own independent research. In support of this, the role will also come with an affiliate title of Research Assistant Professor.
Director - Data Science Institute - Middle Tennessee State University, Murfreesboro TN USA
Reporting to the Vice Provost Research, provide overall administration and programmatic activity for the new Data Science Institute (DSI). This position is charged with bringing in external funding to make DSI self-sustaining within five years of hire. The Data Science Institute's mission is to serve as a catalyst and resource for the pursuit of basic and applied research, curriculum development, and workforce training in emerging disciplines such as cloud computing, data analytics, computational simulation, cyber security, and bioinformatics. The DSI will engage MTSU faculty and students, as well as external private and public stakeholders in data science fields. The incumbent will perform other related tasks and special projects as assigned. This position is contingent upon securing external funding to self-sustain DSI within five years.
Executive Director of Data Science & Machine Learning Operations - Rice University, Houston TX USA
Rice University's Office of Transformational Technology and Innovation seeks an Executive Director of Data Science and MLOPs to play a pivotal role in developing and deploying innovative technology solutions. This role represents a unique opportunity to influence the future of technology and innovation at Rice University, fostering an environment that encourages curiosity, creativity, and a relentless pursuit of excellence in serving our campus community.
Operations Director for Cryo-EM Core - Johns Hopkins University, Baltimore MD USA
The Johns Hopkins University invites applications for Operations Director of the Cryo-EM Core located on the Homewood Campus. The Operations Director of the Cryo-EM shared resource core at Johns Hopkins University (Homewood Campus) is responsible for the scientific and strategic oversight, as well as the implementation of state-of-the-art technologies for cryo-EM imaging, in support of research projects of a broad spectrum of faculty from our institution. The Operations Director will interface closely with their counterpart, the Operations Director of the cryo-EM core at the School of Medicine Campus, to implement a world-class cryo-EM facility across Johns Hopkins campuses, which together will function as an integrated team, sharing expertise and support. The Operations director will maintain a high level of professional expertise and scientific networking within the global cryo-EM community.
Principal Research Software Engineer, National Deep Inference Fabric - Northeastern University, Boston MA USA
We are seeking a highly skilled Principal Research Software Engineer with experience in Machine Learning and Large Language Model interpretability research methods, to assist in developing the National Deep Inference Fabric, an open-source deep learning interpretability research computing infrastructure project. You will be responsible for full stack development, doing both back-end and front-end software development to help create a robust, high-throughput, highly usable, and flexible multi-tenant AI inference service to enable research nationwide. Some of the day-to-day activities include solving security, stability, integration, and performance issues involved in providing a large-scale research inference service for open-source AI models. We are looking for someone who can implement state-of-the-art parallel GPU inference methods, and incorporate them into a system with job scheduling, routing, quota management, authentication, authorization, and telemetry to create a high-performance computing infrastructure. This person should be expert in Python and working internals of PyTorch along with Unix/Linux service development, HPC/cloud environments, and all other aspects of the software development life cycle.
Manager, Statistical Support Services - Yale University, New Haven CT USA
The Manager of Statistical Support Services is responsible for the StatLab, the library’s research support program in statistical methods and analysis. Through a robust a consultation and training program, the StatLab offers research support to faculty and students across Yale. The incumbent will mature the StatLab service model to ensure the program is sustainable, that it enriches the University’s growing research support ecosystem, and that it meets the existing and emerging needs of students and data-intensive researchers. To keep the program responsive and relevant, the the manger will implement a robust assessment and user engagement plan.
Program Manager, Natural and Artificial Minds (NAM) - Princeton University, Princeton NJ USA
The Princeton Laboratory for Artificial Intelligence (AL Lab) is a recently established administrative unit dedicated to supporting and advancing high-impact AI-related research initiatives at Princeton. The current initiatives within the AI Lab include Princeton Language and Intelligence, AI for Accelerating Invention, and Natural and Artificial Minds. Princeton Language and Intelligence (PLI) advances fundamental understanding, informed design, and cross-disciplinary applications of large language models. AI for Accelerating Invention, (AI)², integrates artificial intelligence into engineering research, developing new technologies and tools to accelerate the process of invention — from design, to simulation, to fabrication, to control. Natural and Artificial Minds (NAM) takes advantage of advances in AI to study natural and artificial minds in parallel with a focus on problems of learning, reasoning, decision making, and action. The AI Lab is seeking a Program Manager for the initiative Natural and Artificial Minds (NAM), to provide administrative support for its research initiatives. The Program Manager will assist faculty, research scientists, post-doctoral fellows, research software engineers, and graduate students involved in the initiative. Reporting to the AI Lab Executive Director, the Program Manager will oversee the operations of the Natural and Artificial Minds (NAM) initiative while contributing to the overall success and strategic goals of the AI Lab.
Program Manager, AI for Accelerating Invention - Princeton University, Princeton NJ USA
The Princeton Laboratory for Artificial Intelligence (AL Lab) is a recently established administrative unit dedicated to supporting and advancing high-impact AI-related research initiatives at Princeton. The current initiatives within the AI Lab include Princeton Language and Intelligence, AI for Accelerating Invention, and Natural and Artificial Minds. Princeton Language and Intelligence (PLI) advances fundamental understanding, informed design, and cross-disciplinary applications of large language models. AI for Accelerating Invention, (AI)², integrates artificial intelligence into engineering research, developing new technologies and tools to accelerate the process of invention — from design, to simulation, to fabrication, to control. Natural and Artificial Minds (NAM) takes advantage of advances in AI to study natural and artificial minds in parallel with a focus on problems of learning, reasoning, decision making, and action.
Associate Director High Performance Computational & Data Ecosystem - Edify Technologies, New York NY USA
The Scientific Computing and Data team at the client partners with scientists to accelerate scientific discovery for basic and translational science research. To achieve these aims, support a high-performance computing and data ecosystem along with MD/PhD-level support for researchers. The group is composed of a high-performance computing team, a research clinical data warehouse team and a research data services team. The Associate Director, High Performance Computational and Data Ecosystem, brings a strategic, tactical and customer-focused vision to evolve Sinai’s computational and data-rich environment to be continually more resilient, scalable and productive.
Inaugural Director, AI Innovation Institute - Stony Brook University, Stony Brook NY USA
The inaugural leader of this University-wide institute will join Stony Brook at an extraordinary time, as the University solidifies its flagship-campus status within the SUNY system and begins the deployment of its strategic plan, Our
Moment, which prioritizes growing the research enterprise among four primary goals. Leveraging unprecedented new funds generated through enrollment growth, increased state support, and historic philanthropic giving, Stony
Brook is undertaking high-profile initiatives. These include becoming a flagship campus in the 64-campus SUNY system, serving as the anchor institution in New York’s new climate solutions research center on Governor’s Island, and launching AI3, which builds on the University’s role as a core partner in Empire AI, New York State’s $250M investment in artificial intelligence and related computing infrastructure. These successes are generating resources and excitement and creating openings for partnership, scale, and broader impact for the University in research, education, and outreach. Capitalizing on this momentum, the director of AI3 will lead Stony Brook forward in the rapidly expanding space of artificial intelligence.