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quantitative background. Demonstrable interest and experience in theoretical neuroscience. Obtained a PhD in computational neuroscience, physics, mathematics, computer science, machine learning or a related
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Applications are invited for a fully-funded 3-year PhD studentship based in the Department of Clinical Neurosciences at the University of Cambridge under the supervision of Dr Topun Austin starting
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. Familiarity with standard design verification (DV) procedures and continuous integration (CI) setups would be beneficial. Knowledge of machine learning workloads and the design of machine-learning accelerators
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completion, preferably with an application of their research in a real-world setting. Coding and software engineering proficiency will be expected if relevant to their experience, e.g. for machine learning
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used. AI methods for generating regulatory hypotheses between genes, hormones and physical properties will also be developed. Applicants must have/be close to obtaining a PhD or MPhil in Computational
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(e.g., Reinforcement Learning, Agent Based Modelling) to join our team full-time as part of a large international collaboration of European researchers (incl. Tobias Dienlin, Veronica Kalmus, Adrian
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fixed-term post, and is to provide cover during the absence of another staff member. This post requires teaching expertise at a level that is at the forefront of academic practice. Ability to teach
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), electrophysiology (EEG), interventional (TMS, tDCS) and neurocomputational (machine learning, reinforcement learning) approaches to understand the network dynamics that support learning and brain plasticity
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We are seeking an experienced and reliable individual to provide comprehensive administrative support to the IMS-MRL PhD and MPhil courses and teaching team to ensure the smooth and efficient
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machine learning tools and working on Linux High-Performance Computing platforms would be highly desirable. This is a highly collaborative role and you will work with scientists and clinicians from other