40 machine-learning "https:" "https:" "https:" "https:" "https:" research jobs at Nature Careers
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interests in applied statistics, machine learning, or computational biology are encouraged to apply. For more information, please visit our website https://ds.dfci.harvard.edu/postdocs to view the list
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infrastructures organized in infrastructure platforms, of which the Vibrational Spectroscopy Core Facility (ViSp) is a central infrastructure for this project (https://www.umu.se/en/research/infrastructure/visp
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. Experience in high-throughput sequencing data analysis and cluster/cloud computing. Proficiency in variant calling, single-cell DNA and/or RNA analysis, and machine/deep learning (preferred but not required
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, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
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/performance trade-offs and typical RAN levers; experience with energy metering data is a plus. • Strong background in AI / Machine Learning for decision-making (e.g., forecasting, optimization with learning
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, or comparable research experience, along with significant experience in machine learning, computer programming, computational biological applications. A strong background in statistics and biology. Experience
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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, or a closely related field Strong programming skills, e.g., Python, and familiarity with machine learning and/or software engineering workflows; experience with Git and empirical evaluation Experience
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hold a PhD in oceanography, marine ecology, computer sciences, data sciences or similar. We expect that you have: Expert knowledge on network modelling, especially aimed at ecological applications Strong
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-body physics nonequilibrium quantum dynamics, to quantum computation, quantum information, and machine learning. The Institute provides a stimulating environment due to an active in-house workshop