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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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analysis and machine learning methods for optimisation and decision making, to describe the F&V supply chains for various products at regional UK scale and assess their resilience to cascading risks
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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You
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Experience in machine-learning modeling for solid mechanics applications Experience in the development and coupling of numerical methods for solid mechanics modeling Experience in digital rock technology
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: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing
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machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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, Lancaster and Sumter enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service
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Previous Job Job Title Post-Doctoral Associate - Electrical and Computer Engineering Next Job Apply for Job Job ID 369523 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD