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will have a PhD (or close to completion) in engineering, materials science, physics or other closely-related disciplines. You will be a highly motivated and responsible researcher with strong curiosity
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evaluations, attacks on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related
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. You should hold a relevant PhD/DPhil, or be near completion, in electrical engineering, computer science, applied mathematics or another related area. (For candidates with an undergraduate/Masters
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should possess a PhD/DPhil in engineering, physics or applied mathematics, and hold a strong publication record in your field. You should have expertise in numerical or experimental modelling (preferably
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the basic mechanism(s) underlying triboelectrification processes by leveraging multimodal experimental techniques. You should hold a PhD/DPhil (or near completion*) in materials engineering / materials
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bioengineering, biomedical engineering, biophysics, biotechnology, biomedical science, biomechanics, mechanical engineering (with biomechanics), bioacoustics, or other related area. You must have experience in