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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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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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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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multimodal data. Your responsibilities include: Developing and applying machine learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing
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and document the mechanisms through which LLMs identify software vulnerabilities, creating an interpretable detection framework that provides insights into model decision-making processes. Human-in
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at the University of Luxembourg and at a local international company. The project aims to contribute to sustainability by understanding physical processes during processing of recycled PET (Polyethylene Terephthalate
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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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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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learning models) in these tasks. These investigations include the feasibility, practicality and success evaluation of prototype implementations. More generally, the PhD thesis is part of a large initiative
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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