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, computer science, or ecology with a quantitative, and empirical orientation. Required knowledge, skills and abilities: Ability to multi task and work cooperatively with others Good communication and computer skills
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(or near completion) in computer science, machine learning, statistics/biostatistics, computational biology, data science, physics, or a related field. Experience with modern deep learningframeworks (e.g
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the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025. Appointments are for one
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The Department of Electrical and Computer Engineering at the University of California, Riverside invites applications for a full-time Postdoctoral Scholar position for candidates with expertise in material growth
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Staff Job Description Job Responsibilities 50%: Lab research - Design, plan, conduct and coordinate experiments in a wet lab as well as using computers. 20%: Communication - Keep detailed records of data
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, data science, computer science, or a related discipline. Job Description: The position will focus on carrying out interdisciplinary projects that leverage our comprehensive research databank on UF
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of Applied/Computational Mathematics. Research topics of interest include numerical methods for scientific machine learning and AI, and their applications to various science and engineering disciplines
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)-POSTDOC [#30871] Position Title: Position Type: Postdoctoral Position Location: Chicago and Evanston, Illinois 60208, United States of America [map ] Subject Areas: Computer Science Economics
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program; produce work for publication, present work at scientific conferences, and mentor graduate and undergraduate students. REQUIRED: Ph.D. in Physics, Nuclear Engineering, Plasma Science, Computer Science
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proposals. -Other duties as required. Required Education: -Ph.D. in Soil and Crop Sciences, Statistics, Data Science, Computer Science, Agricultural Engineering, or a closely related field. Required Knowledge