402 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" scholarships in United Kingdom
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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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to commence on 1st October 2026 are now open at the School of Physical Sciences. The projects available are listed here: https://www.open.ac.uk/science/physical-science/phd-students/current-phd-studentships
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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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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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data set (e.g. neutron irradiations, that take years/decades to generate). Digilab brings AI/ML (artificial intelligence / machine learning) approaches for data engineering and automation to utilise
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). The successful applicant will be expected to contribute to the work of the subject area as follows: Teach on/contribute to established course units on the BA(Hons) Art History at all undergraduate levels
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human-animal-computer interaction lens, examining how digital tools shape farmer-cow relationships, perceptions of care, and welfare‑related decisions. The project will investigate how farmers engage with
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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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Modelling, Applied Statistics, Linguistics, Data Analysis, Large Language Models, Machine Learning. Start date: 1st October 2026 Deadline: 30th April Duration: 36 months Funding: Funded Funding towards
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human-animal-computer interaction lens, examining how digital tools shape farmer-cow relationships, perceptions of care, and welfare‑related decisions. The project will investigate how farmers engage with