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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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underlying health and healthcare. They collaborate with partners from all over the world to improve health outcomes for all. Radboudumc unites patient care, research, education, and corporate learning, which
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analyses and machine learning. Some data for the project already exist, but additional data will be collected from behavioural tests on privately owned pet dogs in Sweden and abroad (Europe). Travel and time
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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, optically-pumped magnetometers, to understand how early learning develops. You will address how caregivers’ behaviour influences infants’ learning and how early differences in learning predict language and
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work with large-scale behavioural data sets using a range of approaches, including heritability analyses and machine learning. Some data for the project already exist, but additional data will be
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be completed within four years. You are also requested to teach. You will work with the following supervisory team: - Dr. Frank Harbers (co-promoter) - Dr. Rik Smit (co-promoter) - Dr. Scott A
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, conference presentations, and ultimately a PhD dissertation. The PhD thesis is to be completed within four years. You are also requested to teach. You will work with the following supervisory team: Dr. Frank
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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders