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Field
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, instrumentation, modeling, and data science Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field(s) of materials science, physics, computational science, or a related field
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microscopy, and mammalian cell culture will be an advantage. As a formal qualification, you must hold a PhD degree (or equivalent). We offer DTU is a leading technical university globally recognized
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Vision, Robotics, Evolutionary Computation, Deep Reinforcement Learning, and Machine Learning. This should include a proven publication track record. You should also have: Research Associate: A PhD (or
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
fellow to participate in his inflammatory bowel diseases (IBD)-focused computational genomics research program, part of a collaboration with Dr. Shehzad Shiekh, MD, PhD, faculty in the Division
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: Immunology, Biochemistry, Chemistry, Computational Biology, Biochemical Sciences or related Why MCW? Outstanding Healthcare Coverage, including but not limited to Health, Vision, and Dental. Along with
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: PhD Field: Biology, Genetics, Biochemistry, Chemistry, Computational Biology, Biochemical Sciences or related Why MCW? Outstanding Healthcare Coverage
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that will be valuable for this position include computational modeling of enzymes, biophysical characterization techniques, and bioinformatics methods. The successful candidate will be able to work
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vision, IoT sensors, and blockchain to monitor food quality, safety and animal welfare in real-time and enhance transparency. AI and machine learning will analyse data from pilot sites to identify
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, well-being, and financial security. Benefits include medical/vision, dental, flexible spending accounts, life, disability, retirement, family life support, employee assistance program, onsite health
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, is seeking highly motivated candidates for multiple postdoctoral positions. We are specifically looking for candidates who possess a strong background in theoretical and computational modeling