352 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD scholarships in United Kingdom
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records that can systematically inform preparedness, training and future response. As a result, learning from past events is fragmented, inconsistently captured, and insufficiently embedded into emergency
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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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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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for preparing meetings related to the expertise area * *To also be assessed via interview How to apply Candidates should submit applications via https://apply.kcl.ac.uk and select the History Research MPhil/PhD
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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
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Course: PhD studentship Project title: Exploring a One Welfare approach to human-animal-computer interaction in digital dairy technologies Principal supervisor: Charlotte Doidge Other supervisors
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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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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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through a One Welfare and human-animal-computer interaction lens, examining how digital tools shape farmer-cow relationships, perceptions of care, and welfare‑related decisions. The project will investigate