2,303 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Ulster University" Postdoctoral positions in United States
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position. Appointment Start Date: Ideally on or before July 1, 2026 Group or Departmental Website: https://med.stanford.edu/buckwalter-lab.html (link is external) https://med.stanford.edu/neurology/divisions
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administrative responsibilities. Those hired at more senior ranks may be asked to teach during the course of their appointment, contingent on sufficient course enrollment and prior approval from the Office of
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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods
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https://pubs.acs.org/doi/full/10.1021/acssuschemeng.5c0419 The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and
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FAQ's please refer to our NYUAD Kawader webpage: https://nyuad.nyu.edu/en/about/careers/postdoctoral-and-research/kawader-research-assistantship-program.html For further information or questions regarding
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found at https://postdoc.wustl.edu/prospective-postdocs-2/ . Trains under the supervision of a faculty mentor including (but not limited to): Assists with grant preparation and reporting. Prepares and
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Centers: Stanford Institute for Materials and Energy Sciences (SIMES) Postdoc Appointment Term: Two years Appointment Start Date: July 1, 2026 Group or Departmental Website: https
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Qualifications • Highly recommended by scientists in the candidate’s field of study • Expertise in one of the following areas: Environmental or Performance Physiology, Machine Learning, Motion Analysis, Multiscale
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. Certifications/Licenses Required Knowledge, Skills, and Abilities Excellent written, verbal and computer skills required. Excellent communication and interpersonal skills. Must be able to work independently and/or
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual