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to density functional theory (DFT) calculations and the generation of high-quality atomistic datasets for the development of analytical bond-order potentials (ADP) and machine-learned interatomic potentials
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monitoring in manufacturing environment Develop modular, extensible workflows for data processing Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal
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populations in the context of nanohertz gravitational-wave observations Applying probabilistic inference to gravitational waves, including through machine learning techniques Analyse and interpret research
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at the interface of computation and biology, supported by strong institutional commitment and cutting-edge infrastructure. Research areas of interest include: • Development of novel AI and machine learning
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, characterization, optimization, and autonomous decision-making. Advance Bayesian optimization, active learning, machine learning, scientific models, genetic algorithms, and AI agents in physical laboratory systems
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the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
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, high-efficiency transmitters, low-phase noise RF sources, and other critical radar components. Topics also cover radar signal processing and machine learning, applying advanced techniques to enhance
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Position Description Department of Physics, University of Toronto JOB POSTING – POSTDOCTORAL FELLOW Area of Research: Experimental Astroparticle Physics Description of duties: The University
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logistical functions, and maintaining laboratory spaces and systems. The position works closely with faculty, students, postdoctoral scholars, and staff to ensure a safe, organized, and productive research
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, quantitative characterization, and machine learning-enabled decision-making. The position is associated with the recently awarded ERC Consolidator Grant project “Engineering Multivalency for Superselective