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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 14 days ago
AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models to uncover how gene expression and mechanical forces interact
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-funded DECIPHER-M consortium (9 partners, €9M), we are building multimodal foundation models that integrate imaging, text, and structured clinical data to predict metastasis risk and identify tumor origin
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the High Temperature Gas-cooled Reactor (HTGR) as the most credible Advanced Modular Reactor (AMR) technology. Achieving improved performance requires accurate, high-fidelity modelling to reliably predict power
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University (ASU) invites applications for two postdoctoral scholar positions in the area of mathematical modeling to work with Dr. Nina Fefferman. The first position will focus on modeling dynamics
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at developing and applying multiscale numerical models for the thermal-hydraulic safety analysis of advanced nuclear reactors, with a focus on the prediction of Critical Heat Flux (CHF) in Small Modular Reactors
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for clinical decision support. Activities include: data integration and preprocessing for AI model training; development and validation of predictive Digital Twin models for disease progression prediction, risk
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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, scholarly productivity, and program accreditation; provide timely, accurate insights to support these efforts. Drive development of advanced statistical and predictive models to improve decision-making in
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minimum of 12 months Appointment Start Date: Early/mid 2026 Group or Departmental Website: https://evodesign.org/ (link is external) How to Submit Application Materials: Please directly contact us at
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Vitro to In Vivo Extrapolation of Toxicant Effects on Ovarian Function” and will focus on phthalates and developing models that extrapolate in vitro assay results to predict in vivo effects on ovarian