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Field
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be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD awarded in Electrical or Computer Engineering Knowledge about Deep Learning
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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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About MINDS Centre for Doctoral Training The Machine Intelligence for Nano-Electronic Devices and Systems (MINDS), is a very successful £5M Centre for Doctoral Training (CDT) funding around 50 PhD
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research is essential, and experience working with electronic health records, microbiology, or machine learning would be very welcome. Applications from candidates who do not fulfil the essential criteria
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research plans of the candidate), in visualization and data analysis, cooperative systems, data mining and machine learning, education, didactics and entertainment computing, or Neuroinformatics. Across
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social research in sociology such as causal inference or machine learning or complex panel data analysis. We are seeking excellent applicants with an international research portfolio and network
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. Interests could include geological field-based methods and big data applications and machine learning methods. Research focus will be on feedback processes between erosion, sedimentation, tectonics and
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tools. In this role, you will mainly focus on strengthening our computational pipeline: integrating multiple standalone machine‑learning predictors into a unified, multi‑objective framework capable
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information and advice on best-practice methodologies in machine learning/deep learning. It is essential that you hold a PhD/DPhil (or close to completion) in a relevant quantitative field (e.g. biostatistics
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, Nutritional Sciences and Women's Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research