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improvements. Examples include optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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array of family-friendly and cultural programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer committed
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variety of affiliated hospitals, research centres, and sites, providing unparalleled opportunities to learn, discover and contribute in one's own way. UBC's Postdoctoral Fellows Office (PDFO) is committed
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, information processing, mathematical models, neural networks, learning theory For additional details, refer to the lab’s webpage. Toyoizumi Lab https://toyoizumilab.riken.jp/ * details of the business RIKEN is
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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strengths in experimental soft condensed matter physics or biophysics research within the department. Candidates with expertise in computational physics, including machine learning, applied to study soft
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all over the world, we work together to develop solutions for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2026/postdoctoral-researcher
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. Essential qualifications and experience a PhD (or near completion) in one of the following fields (or a closely related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 2 hours ago
computer package will be used. Course Enrolment (Estimated): 120 Number of Positions: 1 TA Support: 50 hrs per tutorial & per semester Sessional Dates of Appointment: July 1, 2026 – Aug 31, 2026 Class