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with process safety and security concepts, accident modelling approach, and dynamic Bayesian Networks would be advantageous. Willingness to conduct research in a multi-national project team. Fluent in
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gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques); giving mathematical proofs of their correctness and efficiency; building state
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creative – You bring substantial knowledge of statistical (e.g. Bayesian) methods, strong analytical skills, and creativity. Programming skills – You are proficient in Python and/or Matlab. Research
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under varying lighting, fabric blends, and soiling; (5) porting the inference pipeline to an embedded/edge-compute platform; (6) integrating with our robotic pick-and-place cell for iterative field trials
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different species behave in swarms, how they use sensory cues from their environment, bringing new hindsight about the evolution of collective behaviour. You will deploy a multi-camera 3D tracking system in
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evolution of collective behaviour. You will deploy a multi-camera 3D tracking system in natural settings to study insect flight and swarming dynamics across species. This will involve identifying swarming
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different species behave in swarms, how they use sensory cues from their environment, bringing new hindsight about the evolution of collective behaviour. You will deploy a multi-camera 3D tracking system in
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. The group is responsible for the teaching in the track on Technology Law in the LLB in International and European Law and the LLM in Technology Law and Innovation and LLM in Health and Technology Law. The
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, such as eye-tracking, motion-tracking and neuroimaging techniques. It fosters a collaborative, multidisciplinary and supportive research environment with a diverse international staff. You will be
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programme. The position is hosted at the Department of Transboundary Legal Studies as part of the STeP Group. The group is responsible for the teaching in the track on Technology Law in the LLB in