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showcasing the capabilities of machine learning and artificial intelligence within the manufacturing sector, emphasizing their practical application in industrial settings. Under the theme of "Generative AI
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. using programs like PLINK, bigsnpr, regenie, BOLT-LMM, GCTA, LDSC, LDAK, LDpred1/2, PRS-CS, SBayesR, PRSice. Machine learning approaches, e.g. deep learning, autoencoders, XGboost, or penalized regression
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analyses using Danish register data and/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning
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EXPERIENCE RESEARCHER, Open Learning, to work with the product team on identifying, defining, and refining research questions. Will conduct in-depth user research using multiple methods such as interviews
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of this project is to add support for automatic code optimization in Tiramisu. In particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module
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research laboratory. It may also include design of computing systems, including hardware and software, particularly for the emerging artificial intelligence (AI) and machine learning (ML) tasks
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of MIT/Sloan and an understanding of own role relative to all areas; advanced-level computer software skills, including the ability to easily negotiate the internet and perform complex searches; ability
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technologies with emphasis on security-aware circuits, architectures, systems, and design automation technologies and have an excellent knowledge of many computing applications in civilian and military fields
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), efficient geometrical parametrisation techniques. Special interest in technological aspect of offline-online scientific computing, as well as machine learning, digital twin, artificial neural networks
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progressive experience. Preferred Education and Experience: Preferred Education and Experience Master's degree in related area. Knowledge of general University-specific computer application programs. Knowledge