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for the captioned post. Duties and Responsibilities Develop and apply advanced artificial intelligence and machine learning models to real-world data (RWD). Create innovative tools and solutions to extract deeper
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excellence and continuous methodological improvement. Collaborate with the Data Scientist (AI) to integrate advanced AI/ML techniques into the RWE generation pipeline. Requirements PhD degree in Epidemiology
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collaboration with Dr Whelan and the PhD students, machine learning tools for the handling of the Mauve and MUSE datasets. They will also be expected to lead the research into innovative ways in which the machine
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the long term. Is Your profile described below? Are you our future colleague? Apply now! Education PhD degree in remote sensing, preferably with a doctoral thesis on RTM inversion or deep learning in remote
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Intelligence Learning department of the Computing and Data Sciences (CDS) directorate at Brookhaven National Laboratory (BNL) invites exceptional candidates to apply for a post-doctoral research associate
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Your Job: As part of an interdisciplinary project team with researchers from bioinformatics you will work on quantum algorithms for drug discovery. Here, the focus lies on machine learning and
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, disability or protected veteran status. Required Degree Doctorate -Plant Biology, Molecular Biology, or Genomics Minimum Requirements PhD in Plant Biology, Molecular Biology, Genomics or related field At least
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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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) to integrate advanced AI/ML techniques into the RWE generation pipeline. Requirements PhD degree in Epidemiology, Data Science, Statistics, Computer Science, Informatics or related disciplines. Preferably
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Data Scientist (Artificial Intelligence). We now invite applications for the captioned post. Duties and Responsibilities Develop and apply advanced artificial intelligence and machine learning models