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Field
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Do you have a strong technical background in Corrosion, Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols
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steel or aluminium structures Welding, fabrication, or construction automation Experimental structural testing and instrumentation Numerical modelling and simulation AI/data-driven methods for engineering
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/10.14379/iodp.proc.399.104.2025 ), combined with numerical modelling of PHHSs. About the role You will combine quantitative petrographic and petrological techniques (SEM-QEDS) with state-of-the-art, in-situ
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physics, the development of innovative analytical and numerical methods, strong research outputs, and active contribution to the Centre's collaborative research environment. Qualifications Required PhD in
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different approaches. • Construct mathematical models that capture meaning problem formulations, and identify why existing methods are insufficient. • Develop solutions in the form of algorithm design
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(OCSPP), within the Endocrine Disruptor Screening Program (EDSP) located in Durham, North Carolina. Research Project: This research project will develop methods and test a chemical library for thyroid
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practice (e.g., Python/MATLAB/C++ and/or established modelling platforms). Familiarity with asymptotic and multiscale mathematical analysis methods to ground proof numerical simulations Strong communication
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 7 days ago
and optimizing the novel system. Through numerical modelling, the fellow will investigate the potential of coupling separation membranes to plasma to obtain the separate production of carbon monoxide
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. Relevant tasks include mathematical modelling and development and implementation of numerical methods for reactive thermal multiphase flow, and investigation of cases relevant for understanding processes in
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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model