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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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approaches to transform unstructured bug reports into actionable insights that connect to code, support diagnosis, and improve the speed and quality of fixes. Application areas include developer tooling
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dynamics and biomolecular condensates contribute to PD co-pathologies in human midbrain assembloid models. The work combines advanced imaging, molecular biology, and functional disease modeling. Key
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candidate will work on Bug Report Intelligence for the Generative AI Era (BRIDGE) - exploring approaches to transform unstructured bug reports into actionable insights that connect to code, support diagnosis
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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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ML frameworks like PyTorch/JAX Genuine interest in MLFFs, simulation methods, and foundational ML research Desired skills: Experience with atomistic simulation codes: ASE, FHI-aims, VASP, CP2K
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, economics, and other fields, united by a shared commitment to advancing sustainable technologies that benefit society. For more information, please visit our website: https://www.uni.lu/snt-en/research-groups
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administrations, including eBus Competence Center, Emile Weber, GomSpace, Gradel, IEE, Nexxtlab, Telindus, and Ville de Luxembourg. For more information on the ATLAS IPBG Programme, see here: https://edu.lu/wwpy7
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and textual conditions Primary experiments will be conducted in CARLA (https://carla.org), enabling controlled and repeatable evaluation of hallucinations under diverse driving conditions. The PhD
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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