Research Associate – Networks and Systems Security, Machine Learning
Job location
London, United Kingdom
Type
Full Time
Responsibilities:
Undertake high-quality collaborative research, contributing to the success of the research aims.
Take the lead on writing up findings as they emerge, producing and developing reports, and publications in peer-reviewed journals, in collaboration with the research team.
Present, disseminate, and explain our work at meetings/events and contribute to both the internal and external visibility of the Institute.
Horizon scanning across relevant fields for new advancements and methodology
Be a point of contact, supporting PIs in engaging with stakeholders regarding projects and deputizing in meetings where necessary.
Supervise the work of research assistants and Ph.D. interns in the team and provide guidance as required.
Contribute to the preparation of proposals and applications to external bodies, e.g., for funding and contractual purposes.
Developing and applying red and/or blue team strategies in the context of CNI network environments.
Developing red and/or blue team tools, techniques, and strategies that are enhanced or automated using modern AI for control techniques.
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.
Making foundational AI contributions where it benefits the mission objective(s).
Writing papers for submission to high-quality peer review venues (e.g., S&P, USENIX, CCS, AAAI, ICML, NeurIPS, etc).
Requirements & Skills:
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, and an ability to demonstrate the application of offensive and/or defensive techniques.
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, and Stable Baselines. Experience in development suites, systems, and versioning products (e.g., Git, IDEs, Linux).
Track record of the ability to initiate, develop, and deliver high-quality research aligned with research strategy and external stakeholders and to publish in peer-reviewed journals and conferences.
Track record of outstanding research and delivering impact appropriate for an early career researcher
Excellent written and verbal communication skills including the ability to present complex or technical information, and to communicate effectively with diverse audiences.
