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Field
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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normally for 1 year with a provision for further extensions subject to good performance and availability of research funding. Key Responsibilities: Conduct research in federated learning, maritime
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uses. To investigate feature-level just-noticeable difference modelling for machines to facilitate assessment and optimization. To formulate a comprehensive visual feature codec for machine uses
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Design. With some 200 staff and students, it offers a friendly and international work environment Learn more about CQT at www.quantumlah.org Job Description The visiting research fellow will work
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, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt
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development and optimization of plasma-based PVD and CVD processes for advanced material applications. Operate and maintain semiconductor analysis and metrology tools to evaluate thin film and device properties
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related to spectroscopic information evaluation of optimal information content in the alternative analytical modalities evaluation of in-service solvent stability The work will take place in the CO2
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University at Buffalo (SUNY) The University at Buffalo (UB) #ubuffalo is SUNY’s most comprehensive public research university, and an outstanding place to work. UB amplifies ambition for faculty and staff by