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research fellows to join a multi-year research initiative sponsored by the Bezos Earth Fund . This project aims to develop and deploy advanced AI-driven learning, prediction, and decision-making tools
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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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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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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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advising and predictive analytics software for tracking, outreach, note-taking, and referral flags. Performs retention efforts, virtual student outreach, and job-related tasks for some extended evening hours
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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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, and process improvements.? Project and Portfolio Management? Establish and lead a transparent portfolio governance model that balances strategic impact, risk, and organizational capacity.? Define how
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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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model cell membranes. • Establish predictive models for peptide-induced transport and cell membrane deformation. • Create a training hub for young scientists to prosper in a multidisciplinary