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will use a large dataset of P. aeruginosa genomes and experimental metadata to predict key mutations to the organism. The postdoctoral researcher will join the Whelan lab led by Dr. Fiona Whelan
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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. Viktar Asadchy[AALTO] Co-supervisors/mentors: Dr. Victoria Tormo [INDRA] and Dr. Barthès [3DEUS] Objectives To establish an analytical modeling approach for multilayer tunable metasurfaces that captures
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to staff position within a Research Infrastructure? No Offer Description CIATec selects 1 FAPESP Postdoctoral Fellow to work on the development of predictive models and recommendation systems, based
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predictive confidence, including sensitivity and identifiability analyses Compare grey-box models against purely mechanistic and purely data-driven approaches Optimize model performance for computational
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to work effectively in an interdisciplinary team. PREFERRED QUALIFICATIONS Experience with one or more of the following: knowledge graphs, graph machine learning, link prediction, representation learning
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., probability, analysis), eager to conduct cutting edge research in the field of uncertainty quantification, in particular the theory and methods known as predictive Bayes. Predictive Bayes theory involves
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. Combining AI-based prediction (e.g., TCNN, LSTM, etc) with musculoskeletal models to estimate and predict muscle activation and tendon force over short horizons (e.g. ~200 ms). Integrating these predictions
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mining challenges. The overarching objective of this project is to develop computational models that can predict how effectively glycine-based solutions extract precious metals from ore, enabling
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, artificial intelligence and robotics, data science, cognitive and brain science and economics and finance. Leveraging its ‘4-in-1’ model of education and residential college system, UM provides all-round