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the study of plant-plant or plant-invertebrate interactions Experience in nematology or nematological methods Strong quantitative skills (e.g., generalized linear mixed models, permutational methods, Bayesian
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mixed models, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree
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to the development of computer platforms that support the prevention, diagnosis and management of endocrine disease (endocrine digital twins), Participants will collaborate closely with other Doctoral Candidates and
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levels. Contributing to the development of computer platforms that support the prevention, diagnosis and management of endocrine disease (endocrine digital twins), Participants will collaborate closely
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surveys, interviews, and workshops. Produce publishable contributions at the intersection of AI, Human-Computer Interaction, and Digital Health, with targeted venues including AAAI, ACL, NeurIPS, ICML, ACM
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and quality issues in real-time. Supporting Digital Twins: Play a crucial role in developing the next generation of computer platforms that support the prevention, diagnosis, and management of endocrine
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research with ~40 patients living with adrenal insufficiency using surveys, interviews, and workshops. Produce publishable contributions at the intersection of AI, Human-Computer Interaction, and Digital
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to automatically identify, flag, and mitigate data artifacts and quality issues in real-time. Supporting Digital Twins: Play a crucial role in developing the next generation of computer platforms that support the
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-physiology models of interest for: Phenotype classification Estimation of the probability of developing diseases Anomaly detection Early diagnosis. The recipient will learn and apply a vast portfolio of
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, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning, the project will make use of historical radar