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The Research Associate position is funded through a Leverhulme Research Grant. You will join the Evolutionary Biomechanics Laboratory led by Dr David Labonte (PI) at the Department
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resource. The project will combine algorithm development with hardware-aware modelling and evaluation on FPGA-based platforms. The successful candidate will have the opportunity to help define new approaches
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will focus on new numerical algorithms that improve the computational efficiency of flutter constraint evaluations. By accelerating these evaluations, we aim to enable rapid flutter assessments, and
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research in the new and exciting field of topological deep learning and explore its applications across diverse domains. You will develop novel algorithms that leverage topological and geometric techniques
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-driven workflows to support the optimisation and scale-up of chemical processes. This will include developing automated kinetic model generation algorithms, multi-fidelity optimisation strategies and data
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co
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relevant programming languages, frameworks, and libraries) Identify and develop techniques, paradigms, algorithms and required libraries and frameworks to solve software engineering problems Maintain highly
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can improve this. You will work with clinicians across Europe to test your algorithm. You will be responsible for liaising with internal and external collaborators on data collation, perform model