16 phd-studenship-in-computer-vision-and-machine-learning PhD positions at Duke University
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develop research. Extraction of Patient Reported Outcome Measures. • Collaboration with the PhDs at the laboratory, division, department, and University. Development of data-sharing agreements and
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., M.D., D.V.M.) Preferred Qualifications:. Detail-oriented, very well organized, and approach laboratory procedures with critical thinking. Strong initiative and eagerness to learn. Outstanding problem
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focuses on advanced methodologies in abdominal imaging, particularly applications of machine learning and deep learning to medical image analysis. The lab aims to advance existing imaging techniques and
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of experiments, including raw experimental data and laboratory notebooks. Monitor progress of research projects and coordinate with Principal Investigator and Program team to stay on budget and schedule to meet
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. · The appointment is viewed as preparatory for a full-time academic or research career. · The appointment is not part of a clinical research training program, unless research training under the supervision of a
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experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data. Minimum Requirements: Ph.D. in applied
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program. A doctoral degree or equivalent (Ph.D., ScD., DrPH, M.D., D.V.M., DDS etc) in Epidemiology, Biostatistics/Statistics, Bioinformatics, Genomics, or other relevant disciplines. Knowledge in the areas
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program unless research training is the primary focus. Ready to Make a Difference? Apply now and help us build a stronger, smarter, and more connected future. Duke is an Equal Opportunity Employer committed
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laboratory notebooks. Monitor progress of research projects and coordinate with Principal Investigator and Program team to stay on budget and schedule to meet the milestones and deliverables. Follow standards
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computational models of the immune response for multi-scale epidemic models. This position offers an excellent opportunity for recent graduates interested in applying quantitative and computational methods