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-tools.github.io/ ) in the School of Informatics at the University of Edinburgh, on projects in the research areas of Probabilistic Machine Learning, Neuro-Symbolic AI and Deep Generative Models. The candidate will
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partners. The post-holder will build predictive machine learning models to guide the cell engineering work of the consortium. We are looking for candidates with experience in applied machine learning
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for collective intelligence, have a strong commitment to pursuing applied research in a collaborative and inter-disciplinary setting, have an interest in AI, machine learning, mathematical modelling, and/or
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Machine Learning (ML) models and Python script or equivalent, as well as perform room temperature and cryogenic temperature electrical and mechanical measurements. What You Will Do: Perform mechanical
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will initially be focused on developing and evaluating machine learning and statistical modelling tools to predict and classify disease trajectories using large scale health records databases to answer
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for collective intelligence, have a strong commitment to pursuing applied research in a collaborative and inter-disciplinary setting, have an interest in AI, machine learning, mathematical modelling, and/or
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mesoscale materials modelling to carry out meso-scale simulations of Li/Na dendrite growth and mechanical stress-strain behaviour of materials in solid-state batteries, and to develop machine learning methods
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or electronic engineering, machine learning, medical physics or neuroscience. Knowledge of electronics is essential. Strong programming skills (MATLAB is essential) & Phyton (TensorFlow library, desirable
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machine learning and artificial intelligence. • To present your work at seminars within the Laboratory and at external meetings. • To contribute to the development of the processing and analysis