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framework for the joint analysis of large-scale structure (LSS) and gamma-ray data. By combining these complementary probes of the same underlying matter distribution, the project aims to sharpen constraints
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intelligence / machine learning, biostatistics, computational biology, or related subject area A track record of previous publications in bioinformatics analysis of large-scale biomedical data, e.g.: omics
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with training and using protein language models or similar experience with non-protein large language models. Expertise in python and machine learning implementations (e.g., pytorch). Expertise in other
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of Research Experience1 - 4 Additional Information Eligibility criteria We are looking for a doctor in particle physics with less than two years of experience after the PhD. Experience in machine learning and
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. This large multimodal dataset allows us to estimate and test different computational models of the decision and learning processes. One postdoc is currently working on the MEG and iEEG data, and one PhD
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of deep learning for computer vision (segmentation/object detection); ability to manage, clean and document large datasets; interest in glaciology, quantitative geomorphology and/or remote sensing
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protocols using computer-based data collection software, preparing and submitting ethics applications, drafting informed consent documents, and pre-registering studies. - Submit research protocols
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experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in particular simulation-based inference), strong programming