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exchange activities. CAR also stages an international conference, ‘Appearance Matters’, every other year. About you Candidates are expected to be working towards completion or have completed a PhD in health
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable
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’. The role-holder will work closely with medicinal chemists at University of Oxford and pharmacologists at University of Glasgow, applying virtual screening, machine learning, AI-driven generative chemistry
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. Experience with data-driven modelling, machine learning, or AI applications in energy systems is an advantage. Familiarity with modelling of energy networks, district cooling systems, or integrated urban
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veterinary technician simulation training as well as internal and external continuing education. This experience will provide the foundation necessary to: identify the learning needs of diverse audiences
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machine learning techniques to develop emulators for the theoretical predictions of various observables as function of cosmological parameters. The candidate will develop and use skills in topics such as
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recently funded centre of excellence (Integreat). Integreat collects scientists from statistics and computer science and offers a flourishing machine learning community, including many PhDs and PostDocs
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solver who wants to be part of a dynamic team. Information about the Church Lab: Learn more about the innovative work led by Dr. George Church here: https://churchlab.hms.harvard.edu/ , https
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experience with machine learning is a plus. Recruitment is open immediately and will continue until the position is filled. Applicants should send a brief statement of interest, CV, three named references, and
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed