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complementary data from other Mars missions to strengthen current models and provide comparative insights that enhance research conclusions from Hope observations. Develop Machine Learning methods and run
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
walls; iii) Experimental and/or numerical characterisation of the mechanical and seismic behaviour of the proposed connection solutions; iv) Development and calibration of numerical models representative
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to complementary physical properties of thermal, combustion and detonation. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UPR3346-NADMAA-156/Candidater.aspx Requirements Research
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PhD MSCA - Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment Power converters are essential in numerous applications such as industry, photovoltaic systems
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 24 days ago
-tolerance to the insulin producing beta cells, and 2) Development of immunotherapies for T1D. The work makes use of several genetically manipulated mouse models of T1D, as well as humanized mouse models, and
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, and/or numerical mathematics, as well as an excellent command of a programming language, preferably Python or C/C++. The candidate should have an interest in modeling and solving a complex, coupled
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Your Job: This PhD project focuses on modelling and simulating future gas grids, exploring transformation pathways, and developing cross-sectoral simulation frameworks to support informed decision
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trips, plus an elective course. Examples of elective courses could include aqueous geochemistry, geomorphology, soils, glaciology, GIS, or numerical modeling. We are seeking candidates who can demonstrate
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. The successful candidate will contribute to the development of predictive numerical models and experimentally validated designs for intricate mechanical assemblies, deployable structures, compliant mechanisms, and
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datasets, modelling approaches, and performance metrics; develop physics-informed and data-efficient machine learning models to predict sorbent behaviour from sparse and multi-modal experimental data; and