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related discipline. Proficiency in programming languages for data analysis (e.g., Python, R) and experience with machine learning, statistical modeling, and wearable sensor data analysis is desirable. We
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university’s creative, dedicated and close-knit community. We place emphasis on practical problem solving, interdisciplinary learning, a transformative spirit, and collaboration. The Human-Computer Interaction
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competency models in collaboration with content experts. Develops and analyzes the Learning Needs Assessment for the department and clinical areas. Monitors compliance related to mandatory staff education
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competency models in collaboration with content experts. Develops and analyzes the Learning Needs Assessment for the department and clinical areas. Monitors compliance related to mandatory staff education
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 6 days ago
lives. The Theis Lab at the Computational Health Center is internationally recognized for pioneering methods in machine learning, single-cell analysis, and computational modeling of complex biological
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the Job related to staff position within a Research Infrastructure? No Offer Description Job Purpose As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our
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- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute to a corpus of geo-analytical scenarios with
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, early detection of degradation, and residual life prediction. The program integrates physical modeling, machine learning, and data fusion techniques to optimize predictive maintenance, reduce operating
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program (2025.02832.MAD), funded by FCT-Madeira, under the following conditions: I. Scientific Area: Electrical Engineering and Computer Engineering II. Admission Requirements
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models, artificial intelligence, Bayesian models, data visualization, dynamic causal models, dynamic systems models, item response theory, large language models, machine learning, mixture models