60 assistant-professor-computer-science Postdoctoral positions at Baylor College of Medicine
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Job Description Apply now Job Title: Postdoctoral Associate Division: Psychiatry and Behavioral Sciences Work Arrangement: Hybrid Location: Houston, TX Salary Range: Hiring up to $62,232 FLSA Status
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studies. Writes manuscripts and presents results at both national and international professional conferences. Assists with patient consent, and completion of case report forms (CRFs) and assessment
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Job Description Apply now Job Title: Postdoctoral Associate Division: Psychiatry and Behavioral Sciences Work Arrangement: Hybrid Location: Houston, TX Salary Range: Hiring up to $62,232 FLSA Status
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Job Description Apply now Job Title: Postdoctoral Associate Division: Psychiatry and Behavioral Sciences Work Arrangement: Hybrid Location: Houston, TX Salary Range: Hiring up to $62,232 FLSA Status
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prioritizing deadlines and projects. Assists PI in training and mentoring research staff and undergraduate and graduate students as appropriate. Communicates their findings with other scientists at local
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therapeutic strategies. Our research program is highly collaborative and integrative, using cutting-edge experimental approaches involving cell culture system, primary cell and organoid cultures, ex vivo models
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PhD in Computational Biology, Genomics, Bioinformatics, or a related field (e.g. statistics, computer science, or quantitative biology). Experience in applying or developing computational methods
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. Assists in keeping the lab clean and organized, prepares for regulatory inspections, and helps others with experiments as needed. Minimum Qualifications Education Required: MD or Ph.D. in Basic Science
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manuscripts for publications. Develops new research directions. Trains new lab members, junior lab members, and students. Minimum Qualifications MD or Ph.D. in Basic Science, Health Science, or a related field
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. Summarizes empirical research for publication in research journals. Assists in supervision of undergraduate research scholars. Assists in designing data collection paradigms in intracranial electrophysiology