400 computational-physics-"https:"-"https:"-"https:"-"https:"-"BioData" positions at Nature Careers in United States
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SJCRH Be the force behind the cures. From analyzing complex biomedical data in pediatric cancer to creating innovative technologies and computational tools, your work at St. Jude can directly impact
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computational genomics of Alzheimer’s disease. The Sleegers lab is an international team committed to increasing insights into the complex genetics of Alzheimer’s disease and to investigating the translational
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Job Description We are recruiting highly motivated individuals, committed to clinical excellence, to join our growing Hospitalist Medicine Program as a member of the clinical faculty. The rapidly
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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
in mentoring and shaping the lab's interdisciplinary culture. We Are Looking For Someone Who • Has a PhD in computational biology, microbiology, systems biology, engineering, physics, CS, or a related
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Ragon Institute of MGH, MIT and Harvard and the Department of Immunology Harvard Medical School Faculty Position: Computational Immunologist The Ragon Institute of Massachusetts General Hospital
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-related reports. Make recommendations for program or process improvements by providing inputs for systemic changes to program or project management processes for better planning, scope management, and risk
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Education and/or Training: Bachelor's degree in relevant area (e.g., applied mathematics, physics, chemistry, bioinformatics, computer science, computer engineering) required. Master's degree preferred
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Job Description Collaborates with new and existing program partners to support research initiatives, including the collection, analysis, and interpretation of primary and secondary data to address
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Job Description SJCRH The St. Jude Affiliate Program is seeking a motivated and forward-thinking faculty member to serve as the program medical director. The St. Jude Affiliate Program is a network
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. The candidate will lead computational analyses of these datasets, using the laboratory’s suite of existing AI/ML tools to assign structures to unidentified peaks in metabolomic datasets (e.g., https