72 engineering-computation-"https:"-"https:"-"https:"-"https:"-"U.S" research jobs at Nature Careers in United States
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both computational and experimental biology to join our team in reimagining how we discover and deploy drug combinations in the clinic. Our work is highly interdisciplinary, integrating high-throughput
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other cancers, brain cancers are stubbornly resistant. The College of Pharmacy and the Watson Lab is recruiting a computational postdoctoral fellow to help crack this problem using the latest approaches
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Computational Neurogenomics / AI - Postdoctoral Associate Yale School of Medicine | Adams Center for Parkinson’s Disease Research | New Haven, USA Join a mission-driven team at Yale to build a
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and is seeking a highly skilled and motivated Computational Researcher / Senior Programmer to play a pivotal role in shaping its computational infrastructure and culture. This is a central, long-term
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Postdoctoral Research Fellow - Computational Biologist Dana-Farber Cancer Institute Boston, MA Full Time Overview The Collins Genomics Lab at Dana-Farber Cancer Institute seeks a POSTDOCTORAL
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The Senior Computational Researcher will perform analyses using existing, modified, or newly developed tools/methods. Set up tools and troubleshoot analyses using new or modified computational
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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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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 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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. 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