Postdoctoral Research Associate in Network Data Science, Statistics and Probability - London

Imperial College London

London

4 months ago

Applications closed

Postdoctoral Research Associate in Network Data Science, Statistics and Probability Job Type: Full-Time. Starting Salary: £49017 - £57472 per annum plus benefits To find out more about the job please click the ‘apply for job’ button to be taken to Imperial job site

About the role

Applications are invited for Postdoctoral Research Associate positions in Network Data Science, Statistics and Probability to work on an EPSRC-funded programme on Network Stochastic Processes and Time Series (NeST). NeST brings together the Universities of Bath, Edinburgh, Imperial College London, the London School of Economics and Political Science, Oxford and York, with industrial and government partners BT, EDF, the GCHQ, the Office for National Statistics, Microsoft and FNA. Stochastic network data are of rapidly increasing ubiquity in many fields such as medicine, transportation, cybersecurity, the environment, finance, biology and economics, and NeST aims to achieve a step change in the modelling and prediction of evolving, inter-connected stochastic network processes.

What you would be doing

As part of the NeST team, you will contribute to realising a substantial coordinated push to create, develop and apply innovative new models, computational techniques and/or underpinning theory, in response to real applied problems spurred by dynamic networks in many contexts.

Initially, you will be attached to one or two research projects led by one or two Imperial College investigators (Ed Cohen, Nick Heard, Guy Nason or Almut Veraart; and line managed by one of them). As part of the NeST team, you will have access to, and potential opportunities to work with a larger team consisting of additional academics (Marina Knight, Matt Nunes, Gesine Reinert, Patrick Rubin-Delanchy and Qiwei Yao) collectively covering a wide range of research in NeST areas, and a growing cohort of postdoctoral and PhD student colleagues spread over the constituent universities.

What we are looking for

The role requires a candidate with a PhD in Statistics, Applied Probability or a closely related discipline (or soon to be acquired)
*Candidates who have not yet been officially awarded their PhD might be appointed as Research Assistant
Practical experience within a research environment and / or publication in relevant and refereed journals
Advanced knowledge in advanced statistical methodologies
Advanced programming knowledge, preferably in R, Python or MATLAB
Ability to organise own work with minimal supervision
Willingness to work as part of a team and to be open-minded and cooperative.

What we can offer you

The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity
Grow your career: Gain access to Imperial's sector-leading dedicated career support for researchers as well as opportunities for promotion and progression
Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).

Further information

This is a full time and fixed term role for 24 months based at the South Kensington Campus.

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

Industry Insights

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

Apr 9, 2026

Products

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Mar 24, 2026

Jobs

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

Feb 11, 2026

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.