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safety-critical domain, reducing hallucinations and improving robustness and trustworthiness are essential. This PhD targets principled ways to detect, analyze, and mitigate hallucinations in video-based
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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
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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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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