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Field
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with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will also contribute to the development of interactive tools and training materials
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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Description of the workplace Automatic Control is an exciting and broad subject, covering both deep mathematics and hands-on engineering. Historically it has been instrumental in many areas, from
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relevant skills acquired and will also be determined by the funding available. About you Applicants will hold a PhD/DPhil or be near completion of a PhD/DPhil in a subject relative to Structural Biology
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recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs
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laser processing and to bring your ideas in AI/ML to the technology level. You have a solid background in programming (deep learning, reinforcement learning, etc.), electronics, high-speed data
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. This project is in collaboration with Alex Cagan (Sanger / Cambridge) and Raheleh Rahbari (Sanger). Qualifications Basic qualifications (required at time of application) PhD (or equivalent international degree
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quantitative and qualitative methods to generate systematic knowledge about business-government relations and international business relations. To learn more about the research and education of the department
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on two current ongoing PhD projects, design and test methods that selectively stimulate flatfish to emerge from the sediment, facilitating their capture in the net. Together, the two Postdocs will develop
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum