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states, tissue organization, and biological programs, and connect these representations to mechanisms of human disease. Your work will sit at the intersection of machine learning, computational biology
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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have knowledge of software development principles, and machine learning, including deep learning experience, as well as a strong background in research processes, including report writing, and producing
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breeding program. This requires a Ph.D. with excellent knowledge and skills in drones / UAV / UAS data collection, processing, statistical analyses, AI (machine learning, deep learning) and subsequent
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and Computing Sciences, and the Plant-Microbe Interactions group external link in the Department of Biology. The research combines machine and deep learning (AI), microbiome biology, microbial genomics
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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other field or laboratory instrumentation Apply appropriate behavioral, statistical, econometric, causal, spatial, machine learning, deep learning, computer vision, time-series, or mixed-methods
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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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as a member of the GHER contributing to the EU research project COMEDI in a consortium of 11 leading partners in the field of data assimilation and deep learning. A successful applicant will develop
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, Computer Science, Machine Learning, Artificial Intelligence, Engineering, Mathematics, Operations Research, Economics, Finance, or a closely related subject. Preference will be given to candidates with strong