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

Imperial College London
London
2 months ago
Applications closed

Related Jobs

View all jobs

Research Software Engineer: Geospatial Artificial Intelligence (Geo-AI)

Faculty in Applications of Physics, Data Science and/or Engineering to Particle Accelerators (Tenured, F1117A

Data Scientist

Data Scientist

Data Scientist

Data Scientist

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 Imperials 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.

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

Industry Insights

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

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.