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field Strong background and expertise in data science, bioinformatics, network science, artificial intelligence, machine learning, deep learning, or related areas. Solid understanding of AI algorithms
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with the mission of our university. To learn more about the mission, vision, and values of the College of Liberal Arts & Sciences, visit: https://las.illinois.edu . Successful candidates are expected
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Learning. Experience with the deep learning ecosystem and high-performance computing infrastructures. Experience designing and conducting experiments with human participants is a strong plus. Experience in
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Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 2 months ago
candidate is expected to incorporate innovative approaches into their research that connect classical probabilistic models with modern deep learning architectures. Examples include Bayesian deep learning
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(including numerical optimisation, variational methods and MCMC sampling) C3 State-of-the-art deep learning (including transformers, graph neural networks and normalising flows) C4 Use of GPU programming and
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outstanding teaching in JD and postgraduate programmes, innovating curriculum in Equity and Trusts. Supervise and mentor PhD and higher degree students, nurturing the next generation of private law scholars
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on healthcare data. - Experience in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) particularly in Natural Language Processing (NLP) and Computer Vision (CV) - strong record
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, the Postdoctoral Researcher will drive research at the intersection of health data science, multimodal AI, digital twins, computational phenotyping, and responsible AI. PhD must have been received within the last
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We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis. This is a unique opportunity to
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about applying deep learning to decode the regulatory grammar of plant genomes and translating predictions into testable biological hypotheses, we invite you to join the Omics Data Analysis and