Research Associate – Networks and Systems Security, Machine Learning – Defence and National Security

Job title:

Research Associate – Networks and Systems Security, Machine Learning – Defence and National Security

Company

Job description

Offer DescriptionThe Alan Turing InstituteNamed in honour of Alan Turing, the Institute is a place for inspiring, exciting work and we need passionate, sharp, and innovative people who want to use their skills to contribute to our mission to make great leaps in data science and AI research to change the world for the better.BackgroundThe Defence & National Security Grand Challenge at the Turing is developing an ambitious programme of research to create an immune system for critical national infrastructure (CNI). Working at the intersection of machine-learning and cyber security, you will initially focus on either red (i.e., offensive) or blue (i.e., defensive) teaming a CNI network environment. Increasingly you will then work to automate these techniques (e.g., using GenAI and/or deep reinforcement learning), supporting both the ongoing development of an open source CNI environment for autonomous cyber research and an open competition we plan to launch in 2025.Main duties

  • Undertake high-quality collaborative research
  • Writing papers for submission to high quality peer review venues (e.g., S&P, USENIX, CCS, AAAI, ICML, NeurIPS, etc).
  • Developing and applying red and/or blue team strategies in the context of CNI network environments.
  • The application of modern AI techniques including: DRL, GenAI, transformers and attention techniques, multi-agent approaches (e.g., competitive or collaborative training), sample-efficient exploration methods, meta-learning and generalisability approaches, genetic algorithms, explainable and/or interpretable AI.
  • Present, disseminate and explain our work at meetings/events and contribute to both the internal and external visibility of the Institute.

Person Specification

  • PhD (or equivalent experience and/or qualifications) in cyber/systems/network security, computer science, AI, machine learning, statistics, engineering, or a closely related discipline.
  • A passion for networks or systems security
  • A demonstrable interest in machine learning, AI, and data science.
  • Prior experience developing software in a scientific computing context, ideally in Python.
  • Experience in frameworks such as NumPy, Tensorflow, PyTorch, Ray/RLLib, Stable Baselines.
  • Experience in development suites, systems and versioning products (e.g., Git, IDEs, Linux).
  • Track record of outstanding research and delivering impact appropriate for an early career researcher

Please see our portal for a full breakdown of the Job Description.Terms and ConditionsThis post is offered on a full time, fixed-term basis for 3 years. The annual salary is £44,180 – £49,966 plus excellent benefits, including flexible working and family friendly policies,Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant at a salary of £41,352 per annum.The Alan Turing Institute is based at the British Library, in the heart of London’s Knowledge Quarter.We expect staff to come to our office at least 4 days per month. Some roles may require more days in the office; the hiring manager will be able to confirm this during the interview.Successful applicants will be subject to a DSTL research workers check at the offer stage.Equality Diversity and InclusionWe are committed to making sure our recruitment process is accessible and inclusive.This includes making reasonable adjustments for candidates who have a disability or long-term condition. Please contact us at to find out how we can assist you.Where to apply WebsiteRequirementsAdditional InformationWork Location(s)Number of offers available 1 Company/Institute THE ALAN TURING INSTITUTE Country United Kingdom City London (Central) GeofieldContact CityLondon (Central)STATUS: EXPIREDShare this page

Expected salary

£44180 – 49966 per year

Location

London

Job date

Mon, 26 Aug 2024 03:29:12 GMT

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