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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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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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), 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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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
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We are looking for an organised, proactive individual to help in this varied role. The Learning and Development Coordinator will work within the CAM Doctoral Training Partnership [DTP] to provide
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management studies. The Marketing group at CJBS comprises scholars specialising in marketing strategy and modelling, including econometrics, machine learning, and analytical approaches. In
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topics in spin-glass physics, quantised vortices, or physics-based machine learning and neural networks, broadly defined. The successful candidates will have completed (or nearly completed) a PhD in
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, at the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on the development of learning-based control policies that facilitate
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This is an exciting opportunity for an experienced, ambitious, talented, and highly motivated machine learning scientist to join our team within the Precision Breast Cancer Institute https
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Transactions on Probabilistic Machine Learning. A Gelman, A Vehtari, D Simpson, CC Margossian, B Carpenter, Y Yao, L Kennedy, J Gabry, PC Bürkner, M Modrák (2020). Bayesian Workflow. B Carpenter, A Gelman, MD