343 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" PhD scholarships in United Kingdom
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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to undertake a 4-year, enhanced stipend PhD studentship, within the Digital Metal CDT https://digitalmetal-cdt.ac.uk/ at the University of Nottingham. The Centre for Excellence in Coatings & Surface Eng
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.04.2029 Reference no.: 5311 Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning
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. The PhD will combine behavioural experiments, machine learning, and explainable-AI methods to answer questions: Do SR techniques improve human face identification accuracy? How do SR-enhanced images affect
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on the lab can be found at https://www.sysneurodevlab.org/ Eligibility & How to apply: Please submit a formal application using the online admissions portal (via the ‘Apply’ button above) attaching a CV
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electron laser facilities. Contact: For informal enquiries please contact Sam Hay (Sam.Hay@manchester.ac.uk ) or Mike Hough (michael.hough@diamond.ac.uk ). https://www.manchester.ac.uk/research/sam.hay https
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project, please email Erika Mancini (e.mancini@sussex.ac.uk ) or John Spencer (j.spencer@sussex.ac.uk ) References: https://www.sussex.ac.uk/research/centres/sussex-drug-discovery-centre/ https
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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an attractive annual tax-free stipend; Join a multidisciplinary cohort to benefit from peer-to-peer learning and transferable skills development. For full information about the programme, the research projects