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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular
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of greenhouse gases including CO2 and CH4. The PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning for inteGrated multi-parAmetric
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Machine Learning Bioinformatics The successful candidate will contribute to advancing state-of-the-art in data mining and machine learning research with applications in computational biology by: Developing
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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catalysts for the synthesis of a range of industrially valuable compounds. This PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning
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The AITHYRA-CeMM Joint International PhD Call in Molecular Medicine and Artificial Intelligence (m/f
for both life scientists and computational scientists/machine learning experts will start in September 2026. We offer: a highly interactive introductory program in a supportive peer-group environment
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for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion phenomena and link speciation with
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applications for a paid PhD research fellowship position within the field of probabilistic machine learning to be filled earliest by 1 January 2026 for a period of three years. The research project will
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& Machine Learning • Clinical pathways and decision support for patients with acute chest pain • AutoPiX – Explainable Deep Learning for Multimodal and Longitudinal Imaging Biomarkers in Arthritis • Speaking
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This Australian Research Council (ARC) Discovery PhD project addresses Australia’s urgent challenge of restoring soil carbon in semi-arid ecosystems, which cover 80% of the continent and have lost