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letter should be attached as the first page of your resume. Applicants must apply through the UM Careers website to Job ID 281612 for the ETEP Research Fellow job posting. Please note that only U.S
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Number: 16451 School: Harvard Medical School Position Description: We are seeking a highly motivated post-doc trainee to join Dr. Norbert Perrimon's group in the Program of Genetics at Harvard Medical
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 24 hours ago
) Work Unit Dept of Industrial & Systems Engin Work Location Smith Vacancy Open To All Candidates Position Designation Post Doc Employment Type Temporary - Part-time Hours per week 40 Work Schedule Varies
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Fellow to develop and evaluate artificial intelligence methods for physical medical procedures. The fellow will design and implement machine learning models to analyze procedural data, support clinical
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activity consistent with a Research I university. Postdoctoral Fellows teach one course per semester, including one lower, 2000-level course and one upper, 4000-level course each year. Candidates should
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central component of special needs education and contributes to promoting participation, inclusion, learning, and well-being among children, young people, and adults with special educational needs. Research
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you will apply for the position with us. We offer Exciting and meaningful research tasks as part of an ambitious project on mathematical learning difficulties The opportunity to research children's
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an industry partner. Experience with research software, data pipelines, and simulations, machine learning, high-performance computing, CANFAR, or advanced data systems. Evidence of mentoring or supervising
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James’s Hospital Campus Post status: Specific Purpose Contract Hours Of Post: 39 hours per week Salary: This appointment will be made on IUA Post-doctoral Researcher Salary Scale (Level 1) at no higher than
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Intelligence & Machine Learning Develop machine learning and AI models to identify, predict and characterise genome instability patterns. Design generative and predictive computational models to infer mutational