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scientific data, model architectures, and training dynamics influence scientific predictions. You will join a vibrant research environment at TU/e at the intersection of AI, scientific computing, and
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at the intersection of mathematics, computation, and cancer biology. We develop mechanistic, predictive models of cellular decision-making to address fundamental and translational challenges in cancer, including drug
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tools, such as physics-informed climate and weather predictive models, and trustworthy datasets for training and analysis. Its work aims to improve prediction capabilities and understanding of climate
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to study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting
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, regulatory, or multimodal biological data. Support target and mechanism prioritization by integrating model predictions with biological knowledge and external data sources. Work closely with academic partner
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. They will also lead the development of predictive distribution models that incorporate data from the experiment. The project is funded by the USGS CASC. Qualifications Required Qualifications: A completed
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, regulatory, or multimodal biological data. Support target and mechanism prioritization by integrating model predictions with biological knowledge and external data sources. Work closely with academic partner
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bioinformatics for immunology research programs. You'll work at the cutting edge of AI-enhanced immunology, applying deep learning, foundation models, and advanced machine learning approaches to understand how
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to predict nitrogen (N) and phosphorous (P) excretion, and this was published by Fox et al. (2004). Further, those predictions were refined and improved and partition N and P excretion between urine and feces
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to staff position within a Research Infrastructure? No Offer Description CIATec selects 1 FAPESP Postdoctoral Fellow to work on the development of predictive models and recommendation systems, based