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Explore AI/ML solutions to tough physics problems in the Defence domain
This research team is looking for an experienced engineer or post-doctoral scientist to help them explore the use of modern ML techniques for solving problems across defence related domains such as RF, edge AI and AI security. They work collaboratively within their team as well as with other teams working on similar problems spread across the country. Their work is highly experimental, and it is understood that not all projects succeed, even failed projects contain valuable insights.
You will be building upon cutting-edge ML techniques such as transformers and reinforcement learning to create novel multi-modal solutions. Examples include sensor fusion systems, physics-informed neural networks for simulations, and multi-purpose autonomous robots. Projects will be defence focused but may include offensive capabilities.
**Please note, as projects are defence related, you will need to qualify for UK security clearance to be considered for this role.**
Requirements:
- PhD or equivalent professional experience in a field that demonstrates significant understanding of both computer science and advanced statistical or numerical methods
- Practical experience applying ML techniques to solve real-world problems
- Knowledgeable in conducting and publishing (as first author) high-quality research for academic journals
- Team leadership experience
While not required, both a good understanding of RF physics and previous involvement in defence related research projects would be highly beneficial.
This team has an academic and welcoming work environment where ideas are judged on merit and good work rewarded fairly. Due to the research heavy nature of projects, the team can often work from home and be in the office as little as one day each week. The office itself is located in central London very close to major public transport links making it an easy commute from either within London or the surrounding area. Initially this is an 18-month contract with the expectation of extending this as more funding is released.
Keywords: AI, ML, RF, EM, GNN, Transformer, Autoencoder, Reinforced Learning, Multi-Modal AI, Sensor Fusion, Python, PyTorch, Radio Frequency, RF
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