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Number U250093 Posting Link https://www.ubjobs.buffalo.edu/postings/59894 Employer University Affiliates Appointment Term Position Type Posting Detail Information Position Summary This is a NIH, NLM T15
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project We are seeking outstanding candidates for a Post-Doc position in the field of Shallow Geothermal Energy Systems. As the urgency to combat climate change intensifies, innovative solutions
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of Anomalies ” (SODA), newly funded by the Norwegian Research Council and affiliated with Integreat – the Norwegian Centre for Knowledge-driven Machine Learning. We are looking for a motivated candidate, who
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at their institution. The posted UC salary scales (https://www.ucop.edu/academic-personnel-programs/compensation/index.html ) set the minimum pay determined by rank and step at appointment. See Table 5 (https
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
accessed using the link above. Jiawei Zhou Ph.D. The candidate will be co-mentored by Dr. Yanguang (Carter) Cao and Dr. Jiawei Zhou to advance the development of artificial intelligence/machine learning (AI
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
intersecting with psychology and computer science Position Overview This post-doc represents an exciting collaboration between ANU and the Australian government, focussing on the intersection between AI and
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particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in this role? Qualification requirements: The Faculty of Mathematics and Natural Sciences has a
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Thank you for your interest. Please note, the purpose of this posting is to recruit for on-going and future positions. Join the forefront of groundbreaking research at the Beckman Research
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training; and standard cell, biochemical and molecular biology assays Spearhead new technologies and machine learning; and interact with collaborators and team members Prepare data reports for weekly meeting
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uses long timescale molecular dynamics (MD) simulations, integrated with experimental observables (especially cryo-electron microscopy data), and machine learning tools to better capture the dynamics