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- Universitat de Barcelona
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of the SU(N) Fermi-Hubbard model and its low-temperature phases at the microscopic level. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1003
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effectiveness and toxicity of the treatments. Other duties: Develop and validate cancer risk prediction models using deep neural networks based on semistructured data. Develop and validate learning strategies
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environment. Development of models to diagnose and predict battery performance and ageing. Participation in national and international research projects related with energy storage and its integration in
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apply statistical and machine learning models to identify predictive markers of depression. Design and execute experimental protocols related to self- representation and chronic pain. Prepare manuscripts
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products or hydrographic data analysis is desirable Familiarity with joint community and predictive ecological modelling is a strong asset Languages Fluent in English (written and spoken). Catalan or Spanish
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Fundació Privada Institut d'Investigació Oncològica de Vall d'Hebron (VHIO) | Spain | about 1 month ago
experiments and interpret results. Contribute to biomarker discovery and predictive model development. Support data visualization, reporting, and dissemination of findings in publications. Requirements PhD in
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machine learning models that predict soil health and crop performance. The position will exploit datasets integrating biochemical and molecular soil parameters (with a focus on microbiome features from
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techniques to create a model for the early prediction of victimization, offending and the victim-offender overlap, which could be employed in school and residential contexts to identify youth in urgent need
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Molecular Oncology group (http://bit.ly/molonco ) at Institut de Recerca Biomèdica de Lleida
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sustainability of biomaterial manufacturing through safe design methods, machine learning, and predictive life cycle assessment as well as developing machine learning and hybrid digital modeling methods, combining