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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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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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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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Luxembourg) Guidance of a team of student assistants Conceptualizing and implementing behavioral analyses of video-recorded parent-child interactions (e.g., development of coding manuals, training and