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SD-26045-RESEARCHER IN ADVANCED PLASMA-ASSISTED DEPOSITION PROCESS DEVELOPMENT FOR CATALYTIC THIN...
between Luxembourg and France, the FNR-funded postdoctoral researcher will conduct research in the development of advanced plasma-assisted thin film deposition processes for catalytic and
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, materials characterization and device testing. Extensive experience with clean room processes and testing of resonators is required. •Enjoy working in an international team. Solid track of communication
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multimodal signals to improve the performance of multilingual models for low-resource languages, such as Luxembourgish. Particular emphasis will be placed on language-agnostic modalities, including images and
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of Systems Biology and Biomedicine - in the lab, in the clinic and in silico. We focus on neurodegenerative processes and are especially interested in Alzheimer's and Parkinson's disease and their contributing
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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 dynamics (MD) simulations
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
dynamics (MD) simulations of different materials families composed of Ti and C. Titanium carbides, for example, exhibit exceptional hardness, high melting point, wear and abrasion resistance, and many other
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applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered. All qualified individuals are encouraged to apply. In line with
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micro to laminate/structure), including automation, verification, and clear post-processing metrics for stress concentration reduction. Investigate surrogate modelling (AI/ML) to accelerate the micro
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drought, heatwaves and environmental pressures threaten their resilience. While much attention is given to visible canopy decline, the long-term stability of forests is strongly determined by processes
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Networks for MLFFs Implement and test uncertainty-aware loss functions Study calibration and post-calibration for predictive uncertainty Integrate uncertainty modules into MLFF architectures Detecting