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groundbreaking symbiosis of cutting-edge AI combined with human support. To learn more please visit https://www.kcl.ac.uk/research/embrace About the role The Research Fellow in Digital Health & Data Sciences is
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to fundamental biological problems. The postdoc to be recruited to join the Machine Learning for Integrative genomics team at the Institut Pasteur as part of the ERC Starting Grant MULTIview-CELL, will be working
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We are looking to hire a research fellow (postdoc) to undertake a project to develop efficient AI and Machine
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family-friendly and cultural programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer committed to providing
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Fraunhofer IGD and the FBN team to safeguard an efficient collaboration and communication between behavioural biologists and computer scientists. The project is part of the KI-Tierwohl project (https://ki
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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their research careers. They would also be expected to help seek external funding for their work. The postdoc will receive close one-on-one mentoring, opportunities to present results at conferences, and guidance
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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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inference (otherwise known as spectral retrieval), which involves using forward models in conjuction with Bayesian or machine learning-based techniques in order to derive posteriors on parameters of interest