996 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "NORTHUMBRIA UNIVERSITY" research jobs in United Kingdom
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials
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machine learning, spatial audio & audio-visual AI into groundbreaking creative technology. About you We seek a talented Research Fellow to investigate generative audio AI technology for production
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students in the group. Candidates should have strong training in cross-disciplinary applied mathematics, with a demonstrated interest in biology, and experience in machine learning approaches is a plus. We
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As a University of Applied Learning, the Singapore Institute of Technology (SIT) works closely with industry in its research pursuits. This position is situated within the Centre for Immersification
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Foundation, explores how pathogenic bacteria survive and cause infection. Working at the interface of genetics, chemical biology and machine learning, the team develops small molecules to disrupt key bacterial
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of doctoral students in the group. Candidates should have strong training in cross-disciplinary applied mathematics, with a demonstrated interest in biology, and experience in machine learning approaches is a
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responsible for supporting the delivery of various foresight research projects the Centre will be undertaking. This is an excellent opportunity to gain academic research experience and to learn from leading
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thrusts within the lab’s multi-agent security programme. You should possess a completed PhD/DPhil (or thesis submitted by the start date) in Computer Science, Machine Learning, AI, Security, Robotics
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future of formulated polymers. We are seeking a Research Fellow in Computational Chemistry and AI/Machine Learning to advance the state of the art in understanding the degradation and biodegradation
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turbines represented using an actuator-line approach, assess the applicability and limitations of reduced-order models in predicting turbine performance, and develop machine-learning surrogate models capable