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from working closely with its team of post-docs, associated researchers and partners (that range from Microsoft Research to the NHS). For this project you should have a strong interest in AI/Machine
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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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. 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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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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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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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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contribute significantly to these growing fields. This PhD position is ideal for candidates interested in the following areas of machine learning: Geometric learning: exploiting the structure of data (e.g
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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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Project description Electromagnetic (EM) sensing is emerging as a powerful enabling technology for modern high-value manufacturing. Advances in computing power and machine learning now allow us to
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website for more details about our research: https://lewisgroup.org.uk How to apply For informal enquiries, email Jamie at j.e.m.lewis@bham.ac.uk including a copy of your CV. Please apply through