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. In this PhD project, you will: Develop real-time optimization and hybrid AI models for end-to-end multimodal transport planning under uncertainty. Design synchronization, consolidation, and matchmaking
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to experimentally test predicted models would be appreciated. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UPR2357-PATACH-008/Default.aspx Work Location(s) Number of offers available1Company
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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other
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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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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 3 hours ago
system. Climate models are important tools for improving our understanding and prediction of atmosphere, ocean, and climate behavior. We seek candidates with an interest in advancement of radiative
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Modelling Tumour Dynamics and Radiotherapy Response: Towards Sustainable and Energy-Efficient Cancer Treatments A PhD position is available in the Department of Physics at the Universitat Autònoma de
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communication skills are also required. Desirable attributes include experience with PCM systems, bioenergy, IoT-based control, model predictive control, digital-twin development, prototype commissioning
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by investigating decomposition processes under controlled conditions to unravel the mechanisms of peat decomposition, identify the main actors and predict the interaction between these actors and their
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that enhance the development and evaluation of advanced analytical models using health data. This includes methods for prediction, explainability, prediction under intervention, algorithmic fairness, transparent
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, better adapted individuals can be selected at the seedling stage using only genetic data, accelerating the breeding cycle. Incorporating information about plasticity can aid genomic prediction modeling