41 proof-checking-postdoc-computer-science-logic Fellowship positions at University of Michigan
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programming is a plus. Required Qualifications* Ph.D. degree in biomedical engineering, medical physics, electrical engineering, computer science, neuroscience, or equivalent disciplines. Good written and
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be asked to submit letters directly through Interfolio. The recommendation letters should address the scholarly promise of the research program and the applicant's teaching skills. A curriculum vitae
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reflected on your resume. How will that contribute to your success in the BI Innovation Fellowship Program? iii) Outside the scope of your research where you had to problem-solve and demonstrate leadership
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social science fieldwork, such as interviews or surveys Knowledge about data science and computational methods, such as network analysis Familiarity with natural hazards and public health risks associated
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Apply Now How to Apply To be considered for this position, applicants should submit the following materials: a cover letter, CV, one example of scholarly work, one example of a program that reflects
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your current graduate or clinical residency training program. Graduate-level academic transcripts (unofficial is acceptable) Two writing samples, preferably a copy of a previously published manuscript(s
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into the design of a prototype implant to be tested in large animals. The Neural Engineering and Ophthalmology research environments are excellent. The Neural Engineering Training Program and the Vision Research
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research group that integrates insights from psychology, cognitive science, economics, and related disciplines. Our research focuses on motivated preferences for information and the role of beliefs in social
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opportunities to work with other investigators from Michigan and Harvard. Required Qualifications* Ph.D. in Statistics, Mathematics, Biostatistics, and Computer Science. Strong computing skills using R, Python
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molecular biology topics, including neurodevelopment and neurodegeneration, glial biology and bioinformatics. Using a combination of single-cell omics and genetic tools, we particularly focus on exploring