130 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Princeton University
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The Department of Electrical and Computer Engineering invites applications for postdoctoral, or more senior, research positions. The term of appointment is based on rank. Positions
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experience, interests, and vision (i.e., long-term research plans and goals), and a cover letter that includes an explanation of why the candidate is interested in the EEB Postdoctoral Fellows Program, plus
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commitment to interdisciplinary research are especially encouraged to apply. Responsibilities will include: - Developing a computational Artificial Intelligence form finding design framework to shape
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, experience with a variety of programming languages, and familiarity with critical path planning tools, are essential. A Ph.D. in engineering, operations research, computer science, or another related field is
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behavioral paradigms and combined with computational approaches. We are seeking an extremely motivated postdoctoral researcher with background in human or monkey electrophysiology. Studies will include
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computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models of reinforcement learning in the brain and
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represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly. The University also offers a comprehensive benefit program
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you will be asked in the application which positions you are interested in. For further inquiries, contact astropd@princeton.edu. PhD is required. The work location for this position is in-person
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-rated accordingly. The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.
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of terrestrial vertebrates on Earth today with 11,000 species. A Ph.D. in Evolutionary Biology, Computational Biology, or related fields, is required. The work will focus on i) phylogenomic inference of hundreds