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). The position is part of the research project ThermRisk: A Personalized Digital Human Model for Predictive Thermal Risk Assessment. ThermRisk is a collaborative project between three faculties at HVL: the Faculty
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of assembled satellite bus with launch vehicle and pad facilities. Identify, support/coordinate, and/or operate relevant simulations and analysis tools to predict, track, and redirect key mission performance
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"Water Availability, Variability & Scarcity (WAVES)". The group aims to advance a predictive, quantitative understanding of how scarcity emerges and evolves across regions and seasons by developing
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for understanding their reliability and for making informed decisions based on their predictions. This project aims to develop new methods for uncertainty quantification in mathematical and statistical models
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-driven mathematical modeling in a transdiagnostic clinical cohort. https://impact-mh.org/awardees/impact-mh/ IMPACT-Y is collecting repeated measures from approximately 2,400 individuals across multiple
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. To analyze and control thermoacoustic instabilities, we combine fluid mechanics, acoustics, and combustion with methods from control engineering, nonlinear dynamics, and data-driven modeling (machine learning
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University. Where to apply Website https://www.academictransfer.com/en/jobs/363990/phd-modelling-non-co2-greenhous… Requirements Specific Requirements You are/have the following personal competencies and
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preparation, mesh generation, solver execution, and post-processing. Validate CFD predictions against experimental wind-tunnel measurements and establish best-practice guidelines for turbulence modelling and
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 3 months ago
institutes, with the student spending substantial time at each site to leverage their complementary experimental platforms and expertise. The PhD candidate will focus on developing novel control and modeling
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reinforcement learning, learning-based adaptation and control, physics-informed machine learning, learning-enhanced model predictive control, control-theoretic safety and stability guarantees for learning-enabled