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Field
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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, and machine learning methods. Using regression analysis and vector autoregression (VAR) models, the study examines the relationship between macroeconomic variables and the performance of various
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Engineering, Mechatronics, or Robotics, with a heavy emphasis on dynamic system theory, or a closely related discipline. Strong academic background in applied intelligent control techniques, machine learning
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preparation and testing or powder mixtures, and then to devise predictive models (possible using machine learning approaches) for the estimation of mixture properties from pure component propeties. The PhD
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multiple departments within the University of Cambridge as well as the collaborating organisations (RSBP, NIAB and UKCEH). The role holder will investigate machine-learning approaches that advance the core
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application! Your work assignments Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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, enzymology, or molecular biology - Experience with computational methods (e.g. de novo protein design, molecular modelling, machine learning, or bioinformatics) - Experience with biochemical or biophysical
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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computer chips and more sustainable IT technologies. Your research will be to design and fabricate magnetic thin films using the NanoAccess facility, contribute to the development of the ‘optical pen