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their projects. Teach other group members new skills. Stay informed of current literature relevant to academic research projects. Basic Qualifications Required Qualifications: Ph.D. in chemistry, chemical
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, including survival analysis, time-series techniques, causal inference approaches, and/or machine learning methods to large healthcare datasets. Prior experience mentoring or supervising graduate students
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position within a Research Infrastructure? No Offer Description As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity
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. Postdoctoral fellows who are appointed for a period of four years are expected to acquire basic pedagogical competency during their fellowship period within the duty component of 25 %. Project description and
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meaningful change in the world. Learn more about American University Department: College of Arts & Sciences Time Type: Full time Job Description: The Department of Computer Science in the College of Arts and
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sheet evolution, methane hydrate fluxes, or applying machine learning to geosciences to reconstruct glacial histories and project future ice sheet behavior. Please read this interview for more details
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related field Strong background in data analysis, particularly with behavior data, functional ultrasound (fUS) or other neuroimaging modalities Proficiency in statistical analysis, machine learning, and
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to promote equality, prevent discrimination and protect the human rights of each individual in line with equality legislation. We encourage applicants to consult our Dignity and Respect Policy and learn more
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for PhD students and postdocs. Learn more at: https://www.muoniverse.ch/ Who we are looking for Muoniverse positions often serve as bridges between individual research groups and institutions, supported
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern thinking, connecting decades of computer science methodologies with modern data and