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an outstanding environment in which to develop machine learning tools and engage with an interdisciplinary community of researchers with an interest in AI for healthcare. The post holder will have opportunities
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school, and taking part in the supervision of students. A limited amount of teaching may be included (max 20%). Requirements PhD degree in machine learning, scientific computing, statistics, physics or a
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to the group’s open-source software, and participation in the supervision of students. A limited amount of teaching may be included (max 20%). Requirements PhD degree in machine learning, computer science
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presentations, and lab collaborations; mentorship of lab graduate and undergraduate students Qualifications Requirements: A PhD in Computer Science, Information Science, or a related field Must have a strong
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, and machine learning within the scope of ACAG. The postdoc will have various opportunities for professional development, including contributing to and leading grant proposal development (e.g., external
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | 2 months ago
, we will engineer smart CAR T therapeutics for solid tumors. Condensates, Chromatin and Translational Control in Pediatric Cancer (Grebien Lab , CeMM/St. Anna CCRI). We develop quantitative and
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machine learning within the scope of ACAG. The postdoc will have various opportunities for professional development, including contributing to and leading grant proposal development (e.g., external
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 2 months ago
. Our partners are the Medical University of Vienna, the St. Anna Children’s Cancer Research Institute (St. Anna CCRI) and AITHYRA. Selected candidates will join one of CeMM’s or St. Anna CCRI’s research
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://postdoc.wustl.edu/prospective-postdocs-2/ . Lab website: https://cruchagalab.wustl.edu/ . Research Projects: Plasma, CSF and Brain Proteomic analysis. Biomarker identification through the use of machine learning
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis