RCT #183 - We Need To Talk About AI. Plus: Upfront technical requirements are possible; (Too much) Manual work is a bug; Technical governance and leadership; Define your audience better than 'non-experts'; Microvms and Isolation
I think I’ve served you poorly by not writing more about AI.
As many of you know, my current day job is with a company now best known as an AI hardware/software vendor. I prefer to avoid topics where there would appear to be a conflict of interest. That’s a fine goal, but in this case I think I’ve taken it too far. It means I’ve shirked my duty talking about a range of timely topics that all our teams are wrestling with.
Whether wearing this hat or my day-job hat, I talk to a lot of academic leadership. Many VPRs and Deans are freaking out, some less quietly than others, about what they’re going to do about supporting AI in their research institutions - where they should start, what resources should they commit, what to pay attention to and what to dismiss.
And that last part is a real problem for them to suss out. Like all of us, they have limited resources, including time to seek out advice. The conversations they can have most readily are with various genuinely passionate advocates, who think that where they should start is “everywhere” and what resources they should commit is “everything”.
Or alternatively, while their researchers are demanding more support immediately, there are conversations available with others telling them to wait it out, this is all a fad that will pass.
(Research HPC teams, you know I love you, but as a community we’re the worst for exactly that kind of ornery dismissiveness of not-invented-here kinds of research computing. The history of the last twenty years of computing, as narrarated by the median research HPC cluster team, would have gone something like: “Big data - that’s stupid, ignore it and it’ll go away. Cloud - that’s stupid, ignore it and it’ll go away. Containers - that’s stupid, ignore it and it’ll go away. Machine learning - you mean curve fitting? Ignore it and it’ll go away.“ Eventually people stop asking. If what they wanted was a reflexive “no” from a computer person, they could file a ticket with enterprise IT.)
This should be a time for our teams to shine. We’re perfectly positioned to take our rightful places at the table, in discussions on one of the biggest research strategy topics of the day, where we can offer informed, nuanced advice about what’s possible now, where there’s low-hanging fruit, and how and where modest investments (both hardware and personnel) made in our bailiwicks could advance our institution’s research priorities while preserving the other work that’s going on.
But bafflingly many of the teams I’ve talked to have been sitting on the sidelines, waiting for the decisions to be made elsewhere and the consequences to happen to them.
That’s madness. Instead of building trust and demonstrating that we’re expert peers in these strategic conversations, it positions us as junior support players content to do what we’re told.
If decision makers don’t see us as trusted advisors on how best to apply new digital technologies to institutional research priorities, then what is the point of us? If all we do is perform the technical tasks assigned to us, why do they need us to be part of the institution at all when there are so many vendors out there?
I’m seeing a couple institutions build parallel teams and structures right now, explicitly to bypass teams who they don't see as up to the job. It’s rare, but it happens. Each one represents a disastrous squandering of an opportunity, not just for resources but to be seen as worthy participants in research strategy discussions. Advancing research in our institutions and communities is our entire job, and if we’re not willing to take a leadership role in discussions where we have relevant expertise, what are we doing?
As I’ve said before (#104), yes, research should be driving what we do, but sometimes things that happen in our sphere makes possible different kinds of research. We follow, but we need to lead, too. It’s our responsibility to take that leadership when necessary.
When AlphaFold 2 made its big showing in 2020, teams could be forgiven for not really having much of an opinion on what it meant for research. Stable Diffusion didn’t seem obviously interesting to researchers other than in fields like CV. And when ChatGPT came on the scene in 2022, no one really had any good sense of what if anything it might be useful for other than writing poetry like a pirate or cheating on grade-school essay questions.
We’re experts, and we’re most comfortable having discussions in our area of expertise. It makes sense that we’d avoid getting drawn into heated and seemingly hypothetical discussions outside of our experience when it seemed tangential to our mission.
But it’s now summer 2024, and a lot more is known. We don’t need to be experts in a technology to keep abreast of journals and see where people are having important successes.
Just as scripting languages like Python and R made automating work on quantitative data vastly more accessible to researchers and trainees across the academy, LLMs and multimodal models are making automating work on qualitative data like text, images, and videos more accessible. This opens doors for more and larger-scale qualitative research in the humanities and social sciences, but also text- or video-based work in clinical fields.
That’s in addition to successes that people are seeing working in chemistry, or PDEs, or..
At the same time, we’re seeing enthusiastic overreach in those same fields. Work that can’t be readily replicated elsewhere, or models pulling out features intrinsic to the training data set to generalize on. That might indicate real limitations, or it could be researchers lacking solid advice from knowledgable technical teams, or both.
What all this means for our institutions will vary. Existing resources and expertise are different from place to place, and priorities are never the same in any two organizations.
But we have more than enough existing expertise to combine with our deep local understanding of our institutions needs and take leadership in offering advice. We can talk to departments and faculties, give seminars, send relevant papers up the CIO/VPR chain…
Yes, we’re overwhelmed with other challenges and needs, but so are our institution’s decision makers. They need, and deserve, our leadership on this new set of technologies, and the next one to come after that.
We should also have conversations about how our work is likely to change as increasingly the trainees we work with will be coming in having worked with such technologies extensively for two+ years.
I’ll write more about these things in the fall, but some thoughts before I go:
I spend a lot of time talking about changes an RSE service might see when all of the trainees and researchers we work with can generate code instantly. And can ask an LLM to explain code to them. And can have useful “rubber duck” debugging conversations with a rubber duck who is actually quite clever, always available, and also sometimes drunk.
For teaching coding:
- Teaching and evaluation of coding has to incorporate these kinds of tools, because the trainees will absolutely be using them.
- With code-explanation-as-a-service readily available, teaching reading code becomes much easier, and showing learners how to identify high-quality codebases becomes especially important.
- Higher level concepts become more important and usefully happen earlier: When code writing is so fast but accuracy can be spotty, testing fundamentals, version control, and CI/CD becomes vital
- Higher-level architectural concepts also become more important earlier, because integrating generated code with existing code will matter
For writing software:
- Documenting code and giving examples and keeping them up to date becomes even more important, as now those are also instructions for LLM coding tools.
- When glue code connecting well-documented APIs is essentially zero-cost and particularly accurate to generate, plugin support becomes a very useful way to allow extensions to a code base even though earlier that would require pretty sophisticated users.
For research data management, we’ll see acceleration of trends that had already existed: well-curated data sets are going to be even more valuable; policy, governance, and access will be things that get thought about first, not towards the end; and there’ll be more and more pressure for larger datasets, faster.
For research computing systems, we’ll again see acceleration of increasing trends to supporting wide mixes of very different kinds of workloads; complex workflows; standing up APIs and supporting call-outs to external APIs.
And for our day to day as managers, these tools can be useful for telling our stories. When every manager has an automated comms intern who can draft success story blog posts from email chains, generate images (or alt text for existing images), and can summarize presentations and discussions into bite-sized chunks, highlighting our wins and the value we bring to our research institutions become tasks we’d be remiss not to prioritize.
What are you seeing change now? What conversations are you having? Hit reply or email me at jonathan@researchcomputingteams.org.
As suggested above, I’ll be taking off for July and August, so this will be the last new newsletter for a bit. I’m going to try doing something different over the summer, and sending “best of” issues from the archive. Let me know what you think, or if you have suggestions for issues to re-share.
And speaking of the archives, I’ve improved the newsletter archive search (moved to pagefind, for those curious) by quite a bit. Indexing the well over half-million words in the newsletter (not including job ads!) on a static page is going to allow me to do a couple new things later this year, including allowing linking to particular roundup articles or essays. More on that later.
And with that, on to the roundup!
Managing Teams
Over at Manager, Ph.D., in #175 I talked about how useful vacations can be for thinking about how your staff will work without you. It’s a great opportunity to step back, look at what the team is doing, and think about the systems, priorities, and expectations you’re putting in place. Also, in doing so you’re going to find some things you can delegate even when you are around.
In the roundup there were articles that discussed:
- Giving the right amount of context
- How effective feedback nudges behaviour
- That we need respectful disagreement
- Project management mistakes
- Proposing action
- Debugging management
Technical Leadership
Debunking the Myth that Upfront Requirements are Infeasible for Scientific Computing Software - Runku Gupta, BSSW
Debunking the Myth That Upfront Requirements Are Infeasible for Scientific Computing Software - Smith, Srinivasan, and Shanka, SE4Science 2019
Any of these sound familiar?
As the following quotes highlight, previous research has repeatedly shown that many in the community believe that upfront requirements are infeasible for [Scientific Computing Software]: “Full up-front requirement specifications are impossible: requirements emerge as the software and the concomitant understanding of the domain progress.” [5] “Since scientific software is deeply embedded into an exploratory process, you never know where its development might take you. Thus, it is hard to specify the requirements for this kind of software up front as demanded by traditional software processes.” [6] “The research scientists ... do not appreciate the need to articulate requirements fully and upfront as demanded by a staged methodology, and found this articulation very difficult to do.” [7]
I see two opposite issues with planning technical projects among members of our communities, which both stem from the same black-or-white thinking. Either there is vast over-specification and over-planning, where the team then sticks to the details of the plan long after it’s clear the wheels are starting to come off; or there’s the dismissive “we can’t plan this, this is research! It’s the frontier of knowledge! Let’s just get started” sort of rationalizations as seen above.
Either come from a mistaken belief that we either have perfect knowledge or zero knowledge of our goals and the future, and there’s nothing in between.
But that’s goofy. The entire field of project management is one of planning and execution under uncertainty. That’s why risk registers are discussed so often. Every project is uncertain. Any project may have to take unexpected changes in direction. Every project starts off with the least amount of understanding of what will actually happen right at the beginning.
That doesn’t mean you don’t plan, discuss, and make it clear what success looks like right from the start. Because research is so complex and uncertain, it behooves us to do the basics right and create as much of a foundation for success as we can at the beginning. Not having clearly what a successful outcome looks like at the beginning of a hard project is just dereliction of duty. With no star to steer by, success on turbulent waters quickly becomes very unlikely.
Smith, Srinivasan, and Shanka’s article, summarized by Gupta, does a nice and nuanced job of discussing up-front requirements for scientific software, but the same approach can be applied to many technical projects in research support. No, you can’t perfectly define every requirement, but many things you can, and other things can be sketched out provisionally and revisited.
Manual Work is a Bug - Roscoe A. Bartlett, BSSW
Manual Work is a Bug - Thomas A. Limoncelli, ACM Queue
This touches on something I talk about over on Manager, Ph.D. quite a bit about processes.
I really like Limoncelli’s article and Bartlett’s summary. They both drive home that you can’t jump to automation right away - you have to go through the process once or twice to really understand it and what might happen. But then it’s straightforward to incrementally automate:
This culture can be summarized in two sentences: (1) Every manual action must have a dual purpose of completing a task and improving the system. (2) Manual work should not be tolerated unless it generates an artifact or improves an existing one.
This touches on so many things we do.
And I’d take a pretty broad view of automation. There are two things wrong with manual processes - someone has to figure out what to do, then they have to do it. Part of the “semi-automation” can just be writing down a runbook / standard operating procedure for the process, so people have ready access to a known-good way of doing the thing, even if they personally have to do a couple of the steps. Once there’s a written artifact somewhere, you can just constantly make the next time better (Manager, Ph.D. #153).
We have too few resources available to us to squander them on figuring out how to do, then manually doing, thankless tasks.
Product Management and Working with Research Communities
Switzerland mulls research infrastructure governance body - Emily Twinch, Research Professional News
Scientific leadership should be responsible for data platforms - Kenny Workman, Latch Bio
These two recent articles, read together, point to a key tension in our roles, and one that I spend a lot of time thinking and writing about.
Twinch’s article points to yet another example in a welcome trend: decision makers recognizing the importance of a coherent, consistently funded approach to developing, maintaining, and growing our teams and infrastructures, which are key components of any modern research and scholarship programme:
Infrastructures are “fragmented, heterogeneous and geared to short-term funding cycles”, says Swiss National Science Foundation
Our teams are technical experts and professionals and need a governance and funding structure which reflects and builds on that technical expertise and professionalism.
But also, as Workman points out:
The current standard is to delegate [responsibility for technical research platforms] to a computational lead who is further removed from the scientific goals of the organization by training and detached from the daily behavior and needs of the end users. This has led to the widespread deployment of bloated, buggy, and often disjointed platforms that are never truly adopted by research teams and take many years and resources to stand up.
The purpose of our teams is the advancement of science and scholarship with the computational experience we bring, not the computation itself. Workman writes from the point of view of leadership in a biotech company, but it applies at least as much in academic or government labs. Major decisions must be driven by specific research needs.
This tension comes up all the time. One of the first questions technical research support leads ask each other when they meet is whether they reports up to the CIO or the VPR. One is better aligned to the operational and funding needs of the team, the other is better aligned to the mission.
To be clear, I think this tension is a feature, not a bug. It absolutely is awkward to (literally or figuratively) have two bosses, and to be pulled in two directions constantly. But it’s the nature of our work. Technology serves the needs of research, but new technologies can also open up venues for new research questions, in a never-ending back-and-forth (#104), and our cross-discipline vantage point can help us translate approaches to new fields (#119). As I wrote about above, navigating that and helping advice about what new research is possible with new technology is just as much a part of our job as shaping what technology we develop to meet current research needs.
As with so many things about our line of work: if it were easy, others could do it. But they can’t, so we do.
The Mythical Non-Roboticist - Benjie Holson
Holson’s article is talking about the problems he sees with periodic efforts to write robotics frameworks for non-roboticists; the argument is that any such effort is fatally flawed but very close to being an effort that is extremely worthwhile.
There’s really useful points here made that I think matter for product management of any sort of technical effort supporting domain (but not technical) experts. In particular:
“Non-[X]” isn’t defined well enough to be useful, for any value of X. I love this quote:
Don’t design for amorphous groups. If you can’t name three real people (that you have talked to), that your API is for, then you are designing for an amorphous group and only amorphous people will like your API.
And two, a reminder to distinguish between the intrinsic complexity of a problem domain and the unnecessary complexity of an implementation of something. Any effort to stamp out unnecessary complexity (even of things we might have become inured to) is valuable! But be wary of trying to hide intrinsic complexity unless you know you can safely reduce the scope of the problem being solved (which again, comes down to targeting your audience better than “Non-experts”).
Research Software Development
When Is Parallelism Fearless and Zero-Cost with Rust? Abdi et al, SPAA ’24
The article is worth reading, but the abstract sums up the results:
The Rust programming language is lauded for enabling fearless concurrency with zero cost: detecting concurrency errors at compile time. […] We find that Rust, with the Rayon library, indeed delivers fearlessness for program phases comprising only regular parallelism, e.g., prefix-sum. However, for applications with any irregular parallelism, the programmer must choose between unsafe code or high-overhead dynamic checks with errors that manifest at run time, leaving the arduous task of parallel programming as scary with Rust as with its predecessors.
This basic issue isn’t unique to Rust. In fact, it’s my complaint about many novel parallelism frameworks, and I’m guilty of some of this myself: if someone demonstrates to you how trivial a parallel stencil computation is in framework X but shows nothing else, run away, because the real challenge is when you’re trying to do something like a tree-based method or use some other sort of dynamic shared data structure.
Emerging Technologies and Practices
Couple neat items on light weight isolation crossed my desk this week. These approaches are increasingly of interest for running complex workflows of not-necessarily-trusted software
Isolate, a long-running effort initially developed by Martin Mareš and Bernard Blackham, is “a sandbox built to safely run untrusted executables, like programs submitted by competitors in a programming contest”. That could be interesting either just for teaching, or for testing out code.
Then there’s How we scale our microVM infrastructure using low-latency memory decompression by Ives van Hoorne, which discusses code sandbox’s approach for decompressing memory on the fly for cloning micro-VMs (for migration, or scaling up). I keep being hopeful for microVMs to gain traction in environments where people don’t want to run docker containers, but have been disappointed so far, partly due to the fact that passthrough of things like network or accelerators remains unevenly supported.
Random
Discovering a 55-year old bug in the first Lunar Lander Game, which in retrospect is the only possible explanation for why I never got the hang of it.
If this affects you, you probably already know about it, but just in case - Numpy 2.0 is out, and it’s going to break some stuff. It’s good and important that occasionally key pieces of the ecosystem break some backwards compatibility - I’ve argued in the past that we don’t do it enough - but it does mean some work for everyone. Periodic reminder that dependencies with pinned versions and CI/CD with a decent test suite are both good things.
Interesting distinction between three ways of using vector instructions - horizontal (do more of the same operations), vertical (do larger operations), or exotic (implement specific operations that would otherwise require branching logic).
Relatedly, orc, from gstreamer, is a runtime compiler for inner loops, and a library of motifs for inner loops which can be compiled down into various targets
Choose your cereal-based terminal color scheeme with root loops, and learn a bit about color spaces while doing it.
When SQLite will and won’t work in production.
Spreadsheet is All You Need - a small transformer in a spreadsheet.
They’re all you need to animate a roller coaster, as well.
That’s it…
And that’s it for another week. 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 week 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 247 jobs is, as ever, available on the job board.
Senior Investment Manager – Digital Research Infrastructure at AHRC - Arts and Humanities Research Council, Swindon UK
The successful candidate will be responsible for leading allocated activities within AHRC’s Programmes division on Infrastructure. This includes overseeing the development and implementation of funding schemes and initiatives, as well as focusing on strategic areas within the infrastructure portfolio. Collaboration with other Team Heads and Senior Investment Managers within and across AHRC teams is essential to coordinate activities effectively. The role also involves providing input into the strategic direction of the team and research programmes, managing existing schemes, and developing and delivering new initiatives.
High Performance Computing Project Manager - Atomic Weapons Establishment, Reading UK
AWE is looking for an experienced and adaptable Project Manager to lead the delivery, installation and commissioning of a new High Performance Computing (HPC) capability. The Project Manager will also be responsible for reporting to the accountable Senior Project Manager for ensuring the obsolescence management of future HPC, Storage, Networks and Workstations capabilities in accordance with the customer’s service level agreement. Your focus will be on taking ownership of assigned projects, addressing any technical and resource issues to mitigate risks, and driving the delivery forwards to time, cost, and quality. You will work closely with the HPC team to ensure capability, the availability of networks around site, and work stations etc. As the Project Manager, you will be the main point of contact for all suppliers, the construction team, and sub contractors. This is a fast paced and interesting area of the business, you will need to be both reactive and proactive, thinking pre-emptively on equipment obsolescence and renewal. This is a unique opportunity to make a real difference to the safety of our nation.
Centre Manager, Data Science - Queensland University of Technology, Brisbane AU
The Centre Manager provides leadership and management of the Centre’s core operational activities, working closely with the Director and other senior leaders to ensure the efficient and effective delivery of Centre’s vision and objectives. The Centre Manager will lead and have responsibility for managing the operation of the Centre, delivery of administrative services, project delivery support and all operational matters. The position has operational responsibility for the Centre’s budget (including operating and discretionary budgets) and collaborates with share services staff across the Faculty and University to execute the effective operations of the centre.
Facility Research Manager, Mass Spectrometry Unit - Western Sydney University, Sydney NSW AU
Westerns Research Infrastructure team is seeking an experienced Facility Research Manager to join their Mass Spectrometry Unit. This unit plays a vital role within the University, supporting various research activities across molecular sciences, biology, chemistry, forensics, and biophotonics. The Facility Research Manager will oversee and maintain the operational performance of the Mass Spectrometry Facility and its instrumentation. Responsibilities include managing the facility, supporting high quality research, providing training for academic and HDR student users, collaborating with Research Services to generate revenue through the provision of sample analysis for external clients, driving innovative research approaches, participating in technical support and committee meetings, and contributing to future strategic planning. Additionally, the manager will handle budgeting, stakeholder engagement, sample analysis, and compliance documentation.
Engineering Manager, GPU Platform - OpenAI, San Francisco CA USA
As we scale the number of GPUs, number of users, and size of OpenAI, having a team dedicated to the infrastructure to support is crucial. This EM will be responsible for one of our compute teams, GPU or general. They will be responsible for the design, deployment, and management of a massive compute fleet that can grow to meet our business needs.
Biomedical and Clinical Data Informatics Research Manager - Nuvance Health, Danbury CT USA
This complex and multifaceted mid-level manager role demands a highly skilled and experienced individual to lead and manage all aspects of biomedical informatics and clinical data for research protocols for the Department of Research and Innovation. The ideal candidate will possess in-depth expertise in analyzing patient molecular data, combining it with Electronic Health Records (EHRs), and applying AI and ML methodologies for statistical modeling and prediction of outcomes. He/She will also have exceptional managerial skills to oversee data coordination staff collaborate across multiple departments, and support clinicians and researchers. The manager will also oversee and support clinical and investigator-initiated research initiatives related to data, collaborating closely with clinicians, fellows, and residents to translate ideas into actionable projects. The role will utilize project management skills to research workflows and implementing efficient data operations.
Software Engineering Manager, ML HW-SW Codesign - Meta, Sunnyvale CA USA
Meta Reality Labs (RL) is the world leader in the design of virtual and augmented reality systems. Come work alongside expert engineers and research scientists to create the technology that makes VR and AR pervasive and universal. Join the adventure of a lifetime as we make science fiction real and change the world. We are looking for a Software Engineering Manager to support a team of HW-SW Co-design engineers and research scientists. The team would drive HW aware optimizations for CV and LLM workloads running on AI accelerators. The span of work involves model optimization, quantization and network architecture search. The team works in collaboration with ML compiler, ML architecture and ML enablement teams and XFN partners to enable highly optimized solutions for AR products. The ideal candidate will have expertise in supporting a full stack model optimization for AI Accelerators.
Manager, Data Science, PROOF Centre - University of British Columbia, Vancouver BC CA
The Manager of Data Science is responsible for leading, overseeing and managing all computational aspects of research carried out at the PROOF Centre of Excellence. This includes statistical considerations, data management, management of computational staff and students. The incumbent will, develop, and conduct statistical and data mining analyses to identify and evaluate predictive, diagnostic, and prognostic biomarkers for various health outcomes. Major responsibilities include being part of the PROOF Centre of Excellence management team; designing and developing data analysis software; managing the Centre’s large and growing collection of molecular data; performing statistical analysis and modeling of big data, including various omic data (e.g., transcriptomics, proteomics, metabolomic, and epigenetics measurements); writing statistical and data analysis plans, reports, research proposals, and publications; and supervising junior computational staff and students.
AI & ML Software Engineering Manager, TR Labs - Thompson Reuters, Toronto ON CA
Thomson Reuters Labs is seeking a AI & ML Software Engineering Manager with experience leading a team of engineers to build artificial intelligence (AI) powered applications for legal drafting and review. As a member of Thomson Reuters Labs, you will have a direct impact on the world by creating new and innovative products that improve the speed and quality of legal practice, transactional law, and more.
Data Science Learning Associate Director - AstraZeneca, Barcelona ES
Are you ready to be a part of a dynamic team that ensures the long-term success of our Data Science Academy (DSA), As the Associate Director, you will work closely with the Data Science Learning Director to deliver an impactful and innovative educational program in data science and related areas, focusing on the R&D community. This is an opportunity to play a key role in supporting adoption of cutting-edge data science methods across R&D, pushing the boundaries of science and making a difference to patients.
Director ITS Research Computing - Rochester Institute of Technology, Rochester NY USA
Reporting to the Chief Information Officer, you will work closely with senior leaders, including the Vice President for Research, deans, and others to align research computing with RIT’s strategic goals and priorities. You will also lead a dynamic team who provides research computing and data management services, support, and consultation to the RIT research community. In addition, you will engage with faculty researchers across disciplines to understand their research computing needs and challenges, and to identify and implement solutions. The successful candidate will be a strategic thinker with an open and collaborative style who fosters innovation, teamwork, employee development, and operational effectiveness.
Product Manager, Bioinformatics - Genomics PLC, Cambridge or Oxford or London UK
Are you ready to bridge the gap between cutting-edge science and product management? We are seeking a dynamic and driven individual to join our team as a Product Manager specialising in bioinformatics. This is your chance to leverage your technical expertise in a collaborative environment where you'll play a pivotal role in revolutionising precision health products. Reporting to the Lead Product Manager, you'll immerse yourself in the work of scientists and engineers, seamlessly integrating pioneering science into our products. Your role will extend to building our sample and data processing applications more broadly.
Lead Data Scientist / Lead Theoretical Modeller / Lead Software Developer - UK Atomic Energy Authority, Culham UK
The Computing Division at UKAEA plays a vital role in fusion reactor research, covering HPC, data solutions, algorithm development, and AI. This role, within the plasma simulation group, applies modern computational methods to areas of plasma physics. We collaborate closely with specialists in the plasma division and are aiming to expand partnerships with institutions in the US and Europe. Key research areas include developing new simulation capabilities, utilising machine learning for reactor design, and deploying uncertainty quantification tools. Candidates need a background in plasma physics or a closely related discipline, plus experience in scientific computing and/or knowledge of machine learning. The roles may involve the opportunity for an extended secondment to a partner institution.
Data and AI Senior Manager - Accenture, Melbourne or Sydney AU
You will work with our comms clients to build and deliver their Data and AI strategies. In addition to key client relationships, you will also be involved in significant engagement across our business, at multiple levels. You will be someone who can identify opportunities for Data and AI and build capability with key teams internally.
Executive Director, Health Statistics and Informatics - Northern Territory Government, AU, Darwin AU
Provide strategic leadership to the Health Statistics and Informatics branch, including oversight of routine reporting of statutory registries, internal/external academic collaborations, Health Informatics, Health Economics and routine reporting of health statistics.
Director, Bioinformatics Core - Beth Israel Lahey Health, Boston MA USA
The Bioinformatics Core Director will have strategic and tactical responsibility for the operations of the core, consultation, recruiting, overseeing core support staff and provision of leadership in developing core resources and providing research support and training for research faculty and staff at BIDMC. The Core Director will lead a team that develops and applies bioinformatics workflows for standard and custom data analysis across a broad range of technologies, such as single nuclei sequencing, spatial transcriptomics, genome variant analyses, transcriptomics or epigenomics. The Core Director is expected to create deliverables that could serve as standards for quality and innovation in broader communities, building informatics tools for data management, omics and imaging analyses within the reality of clinical data integration.
Director of Bioinformatics - Southern Research, Birmingham AL USA
The Director of Bioinformatics will report to the Chief Data Officer. In this role, you will be responsible for overseeing the development, implementation, and maintenance of bioinformatics strategies and infrastructure to support cutting-edge research. You will work with external biobanks, clinical sequencing laboratories and clinical data health ecosystems to aggregate and assimilate molecular datasets and analysis pipelines in the cloud to support a bioinformatic research community. The Director of Bioinformatics will work internally with SR IT, Data Analytics, Medical Informatics, and product teams to develop a clinicogenomic database; and, externally with pharma sponsors to develop collaborative research projects in support of discovery and development programs.
Bioinformatics/Data Science Lead (Senior Manager/Director/Senior Director) - Curve Biociences, San Mateo CA USA
We are looking for a bioinformatics/data science lead to advance Curve’s biomarker discovery capabilities, NGS bioinformatics pipelines, and clinical test classifiers. Your expertise in biology, genomics, and data science will propel our platform development and discovery efforts leading to the successful launch and adoption of new blood testing products for chronic disease patients with unmet medical needs. This role is expected to initially provide hands-on individual contribution but can evolve to have functional leadership for our bioinformatics and data sciences strategy, teams and culture.
Director, Statistics - Abbvie, San Francisco CA USA
The Director, Statistics provides scientific and statistical leadership for assigned clinical development projects. A highly empowered, visible and collaborative role, the Director works in partnership with clinical and regulatory experts to advance medicines to our patients. Lead the statistical support for one or more clinical development projects through own efforts or those of a team. Lead statistical strategy for project development and regulatory submission. Direct and review the development of design, analysis and reporting for clinical or other scientific research programs. Review Protocols, statistical analysis plans, and statistical programming plans. Represent function/department on project team(s) to provide statistical input to compound/drug development and drive alignment with functional management. Partner with other functions (Clinical, Regulatory, Patient Safety, or GMA) to create development strategies for assigned projects. Represent DSS on data monitoring committees. Build interdepartmental relationships.
Product Manager, HPC & Computational Science Environments - Novartis, Cambridge MA USA
The Scientific Data and Products (SDP) group in RX builds and applies excellence in product and data management to continuously improve the impact and value of software and data to Biomedical Research. We deliver intuitive, intentional, and integrated software solutions that create a frictionless user experience. As Product Manager Computational Science Environments, you play a leading role in defining the future of our Research Dry Lab – a fully integrated combination of high-performance computing (HPC) environments, internally built systems and industry standard software, with a focus on applications that comprise the computational environments for community of data and computational scientists engaged in Computer-Aided Drug Discovery (CADD), Structural Biology, Bioinformatics, Image Analysis, and Modeling & Simulation.
Manager, Digital Solutions - Canadian Institute for Health Information, Toronto or Ottawa ON CA
We are an independent, not-for-profit organization and together with our partners we provide essential information on Canada's health systems, enabling decisions that lead to healthier Canadians. CIHI, through the Hub Program, is continuing along its path to create a more unified, efficient, and adaptive approach to how data is processed, prepared and reported. The Manager, Digital Solutions is a critical role as it is responsible for overseeing the implementation of this multi-year program of work, collaborating across CIHI’s business areas and providing strategic advice to senior executives to guide the successful and timely completion of program objectives.
Facility Manager, UW Nuclear Magnetic Resonance Facility - University of Waterloo, Waterloo ON CA
The University of Waterloo Nuclear Magnetic Resonance (UWNMR) Facility Manager is responsible for all aspects of the UWNMR facility operations. The position is accountable to the Chair of the Department of Chemistry to strategically lead facility operations, implement new systems, processes and strategies, as well as management and administration of the state-of-the-art facility located in the Department of Chemistry. As a core facility on campus the facility participates and supports research programs within the Faculty of Science, WIN (Waterloo Institute for Nanotechnology), Engineering, IQC (Institute Quantum Computing) and Velocity, as well as the initiatives in the University of Waterloo’s Strategic Plan.