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with advanced analytics techniques such as predictive modelling and data mining. - Experience with artificial intelligence and machine learning applications in data analytics. - Proven leadership in
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. Deep expertise in predictive modeling, classical ML algorithms (e.g., decision trees, gradient boosting), large language models (LLMs), generative AI, MLOps, and AutoML using frameworks like PyTorch
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of developing a digital twin model of renewable energy generators”, 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), DOI: 10.1109/ISIE.2019.8781529 [4] https://uk.mathworks.com
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fundamental and applied challenges in how unsteady flows generate sound and how that sound can be predicted or reduced. For more information please visit - https://www.unsw.edu.au/research/flownoise Skills
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outcomes, while ensuring access to reliable and affordable energy. The EE Lab applies rigorous evaluation and modeling methods, including natural and field experiments, randomized controlled trials
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$52,021-85,800/year Type of Position Staff Position Time Status Full-Time Required Education PhD Click here for more information about equivalencies: https://hr.uky.edu/employment/working-uk/equivalencies
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nursing science, applied to nursing practice and care processes, including digital care pathways, predictive and preventive models, and data-informed clinical decision-making. The professorship emphasises
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-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction
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) processes within the EIC PATHFINDER project PREDICT by unifying detailed physical models - light transport, surface reaction kinetics, and multiphase reactive flows - into a single CFD framework. The goal is
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activities under the supervision of João Lacerda: Optimization of T cell therapies Phenotypic characterization of T cell population by flow cytometry Development of in vitro cancer models to study CAR T cells