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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong
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to develop an aeromedical dispatch management software as a technology hub that provides data-driven prediction model and an automated dynamic decision model. The successful candidate will be responsible
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modelling approaches to understand and predict immune cell fate decisions identify transcription factor combinations associated with specific immune cell fates contribute to interpretation and presentation
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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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for the analysis of mobility data and to propose novel mobility prediction models. Undertake fundamental and applied research on privacy-preserving information sharing for urban micro-mobility systems. Lead and
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IFREMER - Institut Français de Recherche pour l'Exploitation de la MER | Sete, Languedoc Roussillon | France | 4 days ago
variables to model the responses of the indicator phytoplankton taxa. Axis 3. Which types of lagoons are the most resilient? • Predict lagoon trajectories based on DRIAS climate scenarios up to 2050, using
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predictive modelling in biomedical, clinical, or bioinformatics contexts. A strong Computer Science foundation with experience or interest in biomedical applications and improving human health is essential
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applications for a joint professorship under the Jülich Modell with the Hasso-Plattner-Institut für Digital Engineering gGmbH (HPI) to be filled as soon as possible: Full Professorship (W 3) for Software Testing
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inviting dynamic young scientists, capable of theoretical fracture mechanics and related modeling techniques, to join our team to probe cutting edge issues in fatigue and fracture. Some examples of research
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and machine learning based analyses including predictive modeling and real world evidence generation. Basic Qualifications: MS in computer science, biostatistics, biomedical informatics or related field