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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 2 months ago
learning models for antimicrobial activity prediction (e.g., Weka); - Strong communication skills; - Fluency in English (written and spoken). The candidate must demonstrate interest in microbiology and
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qualitative and quantitative analytical methods to model clinician attention, verbal reasoning, and documentation behaviour Develop and evaluate machine learning models, including unimodal, fusion, and
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methods to integrate transcriptional and cellular dynamics. Analyze large-scale transcriptomic and spatial dynamics datasets. Work in close collaboration with the team's biologists to test predictions from
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predictions across scales. This role requires deep knowledge of the underlying models and practical implementation skills to maximize biological impact. You will lead rigorous model evaluations, implement novel
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, processing, ad-hoc reporting, and predictive modeling. Develop clear, accurate visualizations to support research interpretation. Maintain up-to-date skills in R and STATA. Presentation & Publication Support
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work on research projects employing latent variable modeling and risk prediction methods to better understand substance use related morbidity and mortality outcomes (e.g., overdose, hospitalization
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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soil quality indicators; - Support for the integration of soil data into grazing prediction and plant regeneration models; - Contribution to technical reports, scientific articles, and dissemination
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partners and key stakeholders, including the medical group, hospital, and SOM/HSC; develop rational, data-based and realistic financial models of proposed initiatives to ensure sustainable investment
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, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities in Europe: four in