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Field
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-ordinate multiple aspects of work to meet deadlines. Responsibilities will include the application of health economics methods to patient-level data as well as acting as a source of information and advice
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robots are therefore required. One of the goals of this project is to commercialise. You must have a strong computer science background and have experience in one or more of the following areas: Computer
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for microreactors. This position resides in the Nuclear and Extreme Environment Measurements (NEEM) Group in the Nuclear Energy and Fuel Cycle Division, Fusion and Fission Energy and Science Directorate at Oak Ridge
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survivorship, genetics, stem cell pharmacogenomics, epidemiology, biostatistics, and computational biology. The successful applicant will gain extensive training and experience in genetic epidemiology and
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have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants are expected to demonstrate research experience in the fields of structural modeling and machine-learning
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leading role in mentoring junior students, with the opportunity to take on formal supervision roles. About you Qualifications: PhD in Neuroscience, Computational Neuroscience, Psychology, or a related field
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development. Basic Qualifications: A PhD in computer science/engineering, electrical engineering, data science or a related field completed within the last five years. Experience of AI and efficient computing
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performing microscopy experiments. The candidate must stand during most of the duration of performing behavioral experiments. The candidate must interact effectively with a personal computer for multiple hours
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personnel and purchase requests for lab supplies. Qualified candidates must hold a PhD or equivalent degree in neuroscience, neurobiology, molecular biology, biochemistry, psychology, genetics, pharmacology
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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