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Field
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Details Title Postdoctoral Fellow in Geometric Machine Learning School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Applied Math Position Description A
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(CGEOINT) Postdoctoral Research Fellow works as part of a team of graduate students, post docs, and faculty on research projects related to large-scale urban simulations and geospatial machine learning
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Details Title Postdoctoral Fellow in Machine Learning and Design of Biological Systems School Faculty of Arts and Sciences Department/Area Science Division Position Description A postdoctoral
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that machine learning applications are developed with ethical considerations in mind. Participate in regular meetings with the research group. Required Qualifications* Ph.D. in Electrical Engineering, Computer
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Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
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standards and data protection regulations. This includes managing sensitive data correctly and guaranteeing that machine learning applications are developed with ethical considerations in mind. Participate in
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 15 days ago
, and safety profiles, and how these relationships can be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory
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. This project involves the development and application of interpretable machine learning methods to uncover allosteric regulation of disordered regions in the dynamics of G-protein coupled receptors to enable
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on understanding how tumor cells evade immune surveillance. The lab is particularly interested in: The regulation of natural killer (NK) cell development, function, and anti-tumor activity. CAR-NK cell therapy
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. Researching and developing novel machine learning architectures for integration across multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and