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Ecohydrology Group, which is part of the Environmental Sensing and Modelling Unit at LIST. Collaborating groups are the Water Systems Monitoring & Modelling group at Delft University of Technology and the
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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. A list of 2-3 references (will only be contacted after initial screening) Education Required: - Completion of a Ph.D. or equivalent by the time of appointment in biology, ecology, or another
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planning, and explainable decision support. The PhD will operate across two worlds: The University of Twente — advancing scientific models, algorithms, and hybrid AI methodologies; Thales (the industrial
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
results. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? You will model the mechanical properties and plastic deformation of materials with diverse
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are especially challenging to represent in numerical models of the atmosphere. Clouds affect the Earth’s radiation budget. Changes in their properties, either due to global warming or aerosol pollution, can
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing
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this collaborative project, we will develop a comprehensive strategy combining (1) vasculature-on-chip models, (2) in-depth nanobubble characterization, and (3) tailored imaging solutions to advance cancer diagnostics
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Vacancies 2 PhD positions on Data-Efficient Foundation Models for Vision Key takeaways The Data Management and Biometrics (DMB) group at the University of Twente is seeking two PhD candidates
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systems and models to enhance learning through AI technology. The PhD fellow will contribute to the Technological Advancement cluster by advancing synthetic data generation as a key work of AI LEARN’s