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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 24 days 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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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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, CRCF). It will develop AI tools to map and predict soil health across space and time, accelerate literature reviews, extract best management practices from long-term experiments, and design methods
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hydrogenation, dehydrogenation, and hydrogen transfer reactions. Detailed characterization and kinetic studies will be performed to test computational predictions and microkinetic models, and to refine machine
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identification, i.e. learning of models from measured data, and iii) real-time control, e.g. using the model predictive approach. We are working on several projects with industrial partners across the energy
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, predictive analysis and immersive interactions, among others. Where to apply Website https://www.poliba.it/it Requirements Additional Information Eligibility criteria TITLES AND INTERVIEW Eligible destination
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for the next 2.5 years at the interlink of prevention and prediction of wildfire risk, by contributing to the development of a fundamental physical model to understand the process of fire spread for
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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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the conditions of crop, pasture, and their environment with advanced remote sensing and geospatial technologies; Develops and refines algorithms and workflows for crop and pasture monitoring, modeling, prediction
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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