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Link https://www.ubjobs.buffalo.edu/postings/63868 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.89 Posting Detail Information Position
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-scale nature, complexity, and heterogeneity of 6G networks, we use tools such as artificial intelligence/machine learning, quantum computing, graph theory, graph-signal processing, and convex/non-convex
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Intelligence (CHI-lab, https://www.x-labs.xyz) in the Division of Engineering, New York University Abu Dhabi, is seeking a highly motivated Post-Doctoral Associate to advance cutting-edge research in machine
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quantum chemical methods and machine learning; developing quantum algorithms for computational chemistry on quantum computers; and applying existing and new computational methods to study multiscale
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the equipment, including the annual budget and the personal computer provided by the MARBEC laboratory for modelling and mapping work. - main mission: The researcher’s main role will be to model and integrate
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The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences | Poland | about 2 months ago
3 Aug 2026 Job Information Organisation/Company The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences Research Field Biological sciences » Biology Researcher
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their effective start date. This position is for a post-PhD trainee preparing for a research career path in academia or industry. The planned position will provide a transition to career independence through
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). The researcher should have a PhD/DPhil in robotics, computer vision, machine learning or a closely related field. You have an excellent academic track record in topics relevant to robot perception. A specific
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics