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, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong
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cerebral organoid models to study the impact of environmental exposures on metabolostasis and proteostasis Build and validate machine learning models (in collaboration) for molecular data analysis and
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The Distinguished Research Fellowship Program seeks PhD graduates from underrepresented groups for postdoctoral experience and training in the School of Engineering and Applied Sciences . The aim of the program is to
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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from multiple disciplines and institutions. Required: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong background in machine learning / computer
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning
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of military Service members with extremity amputations. Why should I apply? Under the guidance of mentor(s), you will gain hands-on experience to complement your education and support your academic and
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balance of supervised investigation and work experience in a learning environment that will expose the participant to activities across the drug development process. We are seeking scientists from U.S
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transcriptomic data, that will be integrated with clinical metadata and whole-genome data for developing machine learning models to identify and predict patient factors driving toxicity response and sensitivity
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research interests in one or more of the following subfields: scientific machine learning, optimization, deep learning, uncertainty quantification, (Bayesian) inverse problems, reduced order modeling, high