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Field
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mathematical models with state-of-the-art machine learning techniques to develop forecasting models that are more accurate, efficient, and capable of incorporating complex atmospheric phenomena. This is a fixed
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Engineering, Physical Science, Mathematics, Computer Sciences, Physical Geography or a related subject. • Good level of written and oral communication skills, as appropriate for disseminating research and
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PhD Studentship in: Understanding risks from emerging contaminants (PFAS) to surface water resources
Science, Mathematics, Computer Science or a related subject. • Enthusiasm for research and collaboration with industry • Good level of written and oral communication skills, as appropriate for disseminating
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criteria for the position are: • A good honours degree (or equivalent experience) in Engineering, Physical Science, Mathematics, Computer Science or a related subject. • Enthusiasm for research, and in
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they are based on mathematical and statistical principles dating from the 1970s. Developing these pharmacometric models remains a laborious task where highly qualified staff spend large amounts of time. Aims
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, including Engineering, Physics, Mathematics, and Computer Science – candidates with experience with Machine Learning and/or Crystallography would be particularly suited for this project. This project will
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undergraduate honours degree or Master’s degree with Merit in a relevant discipline (such as Computer Science, Mathematics or others related to the PhD topic). If English is not your first language, you must have
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postgraduate masters’ qualification (MSc) in a science and technology field: Computer Science, Engineering, Mathematics, with specialisation in Computer Vision, Machine Learning and AI If English is not your
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; solid mathematical background and programming skills; preferably, prior experience with publications in computer vision, machine learning and deep learning. The English language requirements must also be
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1st class would be highly desirable. We welcome candidates from a broad range of disciplines, including Engineering, Physics, Mathematics, and Computer Science – candidates with experience with Machine