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Engineering, Data Science and AI theory and practice, ensuring their co-evolution and competent adoption across disciplines to enable transformative, interdisciplinary, impactful data-intensive research
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group of A/Prof Patrick Burr, please visit https://www.patrickburr.com/group In your role you will be responsible for performing ab-initio and molecular dynamics simulations, and for developing inter
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Responsibilities: The successful applicant will be responsible for: Obtaining theoretical results at the interface of geometry and biophysics Designing, implementing, and testing algorithms to model active matter
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the following tasks: Development of new large language models for process modeling Development of new decision-making algorithms Provide regular project updates to principal investigator and funding agency Report
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applications? The postholders will be responsible for: Developing high-resolution regional and global SM datasets using hydrological modelling and data assimilation, including their validation. Analyzing
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algorithms for analyzing electrocardiography, electromyography and movement signals, identifying characteristics and recognizing patterns in everyday activities. Testing and validation of methods developed in
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or international conference. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: Research and development of algorithms for analyzing signals acquired in real time by a system with integrated
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September 2025, referencing the job ID DR/180/25 to Humboldt-Universität zu Berlin, Faculty of Life Sciences, Department of Psychology, Prof. Dr. Sebastian Markett (located: Rudower Chaussee 18), Unter den
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Responsibilities Development of new machine learning modeling approaches Development of new advanced control and optimization algorithms Optimization of carbon capture process operation Provide regular project
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of crystallization and glassy dynamics. In collaboration with theorists Prof. Juan Garrahan (UoN) and Prof. Kranthi Mandadapu (UC Berkeley, USA), the project will build on previous work [J.G. Downs et al PRL 127