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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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, 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
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-DRIVEN OFFSHORE (Grant agreement ID: 101083157). The position will cover an important part of the activities at the Bergen Offshore Wind Centre (BOW) targeting at bridging fundamental and applied research
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defenses which can kill the cancer cell. Through the analysis in this project, we will hopefully understand when and where this dsRNA is expressed in cancer and how to target it for future cancer
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to improve detection capabilities, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning
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dynamic relationships between plasma levels and target analytes in exhaled condensed breath and in interstitial fluid. Research field: wearable technology, biosensors, electroanalysis, hormonal monitoring
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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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temporal and dynamic relationships between plasma levels and target analytes in exhaled condensed breath and in interstitial fluid. Research field: wearable technology, biosensors, electroanalysis, hormonal
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Health, with targeted venues including AAAI, ACL, NeurIPS, ICML, ACM CHI, and ICHI. Contribute to ethical, explainable, and responsible deployment of multimodal LLMs in the management of endocrine diseases