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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Machine Learning for Intelligent Hearing Assistance in Complex Acoustic
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MICADO (the first light instrument of the Extremely Large Telescope). The project provides a collaborative network, engaging with leading experts in optics, astrophysics, and machine learning from
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for observational studies Experience with machine learning techniques for patient-level prediction Prior experience working in distributed or federated data networks Familiarity with open-source research ecosystems
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: · A completed M.Sc. degree in computer science, machine learning, and related fields. · Strong proficiency in English (the working language of the institute). · Capability and willingness
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systems, large multimodal foundation model training and/or finetuning, and continuous learning pipelines. Experience in multi-modality data analysis (e.g., image, video, text). Experience working in
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English Proficiency in machine learning and large omics data analysis is preferred. Where to apply Website https://www.lih.lu/en/job/?value=JA/PDGMB0326/MD/DIIA Requirements Research FieldComputer science