Head of Research Computing Platforms

The Francis Crick Institute
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data science programme lead

Data science programme lead

Senior Machine Learning Engineer

Technical Program Manager - Machine Learning - New York

Lead Data Engineer

Senior Machine Learning Engineer

Wehave an exciting opportunityavailable for a

Head of Research Computing Platforms

to join one of the world's leading research Institutes at a crucial time in its evolution, and play a definitive role in shaping it for the future. You will join us on a full time

,

permanent basis,

and in return, you will receive a

competitive salary starting from £87,400 per annum, with benefits, subject to skills and experience .The Crick's mission is discovery without boundaries; we don't limit the direction our research takes. We want to understand more about how living things work to help improve treatment, diagnosis, and prevention of human disease, and generate economic opportunities for the UK. Much of our research is both data- and compute-intensive and relies on advanced Scientific Computing systems, services, and skills.The

Head of Research Computing Platforms role:The

Head of Research Computing Platforms

will be part of the Information Technology Office (ITO) team and report directly to the Crick CIO. They will work closely with Crick scientists at all levels of the Institute to collaboratively plan and manage platforms that support the current and future needs of the Crick scientific community.The post holder will be responsible for the collaborative delivery of the platforms and services to science through directly managed teams and in liaison with the wider ITO organisation, working very closely with teams supporting Research Data Management and Research Software Engineering & AI to deliver a unified Scientific Computing function to the organisation, as well as with Projects, Architecture, Infrastructure and Helpdesk functions.It is also expected that you will take an active role in working with the Crick partners and other relevant external contacts, in order to explore opportunities for collaborative development and/or sharing of resources.Responsibilities of our Head of Research Computing Platforms:These include but are not limited to:Understand the scientific and research requirements of the Crick's scientific programmes to advise and work in partnership with researchers and the ITO team to deliver scientific computing platforms appropriately to meet their scientific program needs. This will include understanding the general scientific analysis, work-flows, data acquisition, data management and data processing needs of the Institute.Work with the scientific community, formally, by continuing to develop the existing governance structures, and informally, to provide process for allocation of research computing platform resources (storage and compute), so that existing resources are effectively distributed and a forecast of future requirements is developed.Continued development of the strategy for sustainable growth of the Crick's scientific computing platforms. Develop and articulate a plan for the growth of storage and compute capabilities for the 5 years, which, in addition to the research forecasts, takes into account external comparators and trends; the right mix of internal/external provision and the Crick's funding streams.Develop the Crick's strategy for world class data lifecycle management technology solutions and best practices. Work with colleagues across ITO to collaboratively deliver the technology components of the strategy and with researchers to deliver a successful adoption of the underpinning practices that are necessary to its successful implementation.Take the lead in developing a strategy for business continuioty and disaster recovery of the scientific data and systems, ensuring that it reflects the needs of researchers. Develop evaluated options for presentation to researchers and Crick senior management.Establish an external advisory panel, to validate the Crick's strategies in points 3, 4 and 5, above, and to provide feedback and review of the quality of its implementation.Liaise with external groups to develop and/or leverage shared infrastructure for the benefit of Crick researchers and wider collaborations.Work with researchers to explore the use and articulate the value of new technology to store, analyse and present data.Provide support to researchers in their use and development of programmes and algorithms to analyse data, by provision of expertise to support Scientific Computing.As a commissioner of systems, develop strong relationships with the Infrastructure and Helpdesk team in order to jointly deliver solutions for the Institute.Recruit, maintain and motivate team members, to continue to develop a culture of professional excellence across your teams.Use the Crick's project and PMO approaches to support the effective and transparent delivery of projects.Manage the Scientific Computing revenue and capital budgets and support the CIO in management of the overall ITO budget.Provide reporting on the operational performance of Research Computing Platform services to the CIO, the IT Governance Committee and the Scientific Computing Steering Group, using the same tools as the rest of the department.Skills and experience we are looking for in our Head of Research Computing Platforms??????:Proven experience in developing and managing large enterprise-scale HPC platforms in research or academic environments, with a focus on continuous professional development and industry certification.Strong leadership and experience in building teams to provide HPC support services and training.Demonstrated ability to collaborate with many teams, including those not under direct management.An understanding of scientific research processes, especially in bioinformatics, and their IT requirements.Expertise in data lifecycle management technology, particularly for big data such as image data and gene sequences.Familiarity with scientific applications such as CryoSPARC and Alphafold, and programming languages like R and Python.Experience managing teams to support large compute clusters and high-performance cluster file systems, with knowledge of technologies like Infiniband and job distribution systems.Appreciation of general IT services such as identity and access management, databases, web applications, and networking technologies.Awareness of enterprise Linux/macOS/Windows environments, virtualisation technologies such as vmware and oVirt, and public and private cloud platforms like AWS and OpenStack, and container technologies such as Kubernetes and Singularity.Strong problem-solving skills, with the ability to work under pressure and manage strategic documentation, budgets, and vendor relationships.Closing date:

8th December 2024If you feel you have the skills and experience to become our

Head of Research Computing Platforms,

please click ‘apply' today, we'd love to hear from you!All offers of employment are subject to successful security screening and continuous eligibility to work in the United Kingdom.

TPBN1_UKTJ

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.