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, atmospheric signals), data fusion across sensing modalities, and development of scalable machine learning pipelines. Work will be entirely computational and based in Seattle, with no field deployment
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and experimentalists working across species as part of SCENE The Tolias Lab fuses large‑scale systems neuroscience with machine learning to derive principled models of cortical computation. Our newly
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Mechanical, Aerospace or Electrical Engineering, or other related fields specializing in the area of machine learning and dynamical modeling, excellent problem-solving skills as well as good oral and written
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degree in biomedical data science, computational biology, genetics, bioinformatics, machine learning, computer science, statistics, engineering, medicine, or a related field. Strong candidates may have
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. The fellow will also work closely with SCEC Senior Machine Learning Engineer Dr. Lauren Klein Dubin, who will provide day-to-day supervision of the fellow's technical work. The fellow will have opportunities
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
research vision. Minimum Education and Experience Requirements PhD in Biomedical Engineering, Neuroscience, or a closely related field. Required Qualifications, Competencies, and Experience Previous research
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ability to quickly learn and master various computer programs. Strong record of peer-reviewed publications. A PhD, MD or equivalent with prior relevant training in Immunology, Biology, Bioinformatics
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problems or machine learning more broadly. We are looking for candidates with strong mathematical skills and interests. A requirement for the position is a master’s degree in electrical engineering, computer
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(e.g. via machine learning) to qualitative analyses (e.g. via interviews) to support ambitious policies for climate and energy transitions. This position Green hydrogen is key to decarbonizing many hard
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
emphasis on remote sensing. 2. Experience of using multiple sources of remotely sensed data, particularly optical, Lidar, and Radar data. 3. Sound statistical skills and use of Machine Learning/Geospatial AI