52 computer-algorithm "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at Duke University
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systems, social sciences, epidemiology or other health-related fields. Demonstrated proficiency through previous developed analytical codes with R Language for Statistical Computing and/or Python The ideal
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may include teaching responsibilities. The appointment is generally preparatory for a full time academic or research career. The appointment is not part of a clinical training program, unless research
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the individual's research skills for his/her primary benefit. This postdoctoral appointment is part of the Duke University Aging Center’s NIA-funded T32 Postdoctoral Research Training Program. This
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healthcare. Qualifications Required: PhD (or equivalent) in computer science, statistics, biostatistics, electrical/biomedical engineering, or related quantitative field. Strong background in machine learning
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scientific programming (Python, MATLAB, C/Fortran, or equivalent) and high-performance computing workflows. Proven ability to work across disciplines and manage complex modeling tasks. Excellent written and
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experience in optics, computation and electronics. Responsibilities will include designing and implementing optical systems based on quantitative phase imaging with digital holography and electronic systems
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program, unless research training under the supervision of a senior mentor is the primary purpose of the appointment. The Postdoctoral Appointee functions under the supervision of a mentor or a department
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. Occupational Summary The David Lab at Duke University (www.ladlab.org ) is recruiting a postdoctoral fellow to join an established research group developing and applying DNA sequencing and computational
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multiple affiliations. Postdoctoral FellowPosition Computational approaches to malaria parasite antigen diversity Duke Global Health Institute Be You. The Malaria Collaboratory is recruiting an exceptional
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments