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platform for innovative materials by combining advanced experimental techniques, physics-based mesoscopic modeling, and artificial intelligence. Within this context, high-throughput experiments will provide
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discipline Apply: https://mgician.eu/research/doctoral-candidate-projects/dc3/ DC4: Atomistic Modelling and Design of Thermoelectric Materials and Interfaces Host: King’s College London (KCL), London
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the design of optimized electrodes for more efficient and sustainable hydrogen production. The project is interdisciplinary, combining chemistry, physics, and materials science. Where to apply Website https
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website at http://www.archives.gov/veterans/military-service-records/ *Please Note: As part of the first round of screening, the committee will conduct an anonymous review of the application materials and
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candidate will join a consolidated research group at the University of Oviedo. The work will include numerical modelling of structural materials with applications across various industrial sectors. Where
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and image analysis within the project, responsible for designing and iterating on machine learning architectures, managing training pipelines and datasets, and optimizing models for deployment across
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the Biomechanics, Wood, and Bio-based Materials axis of LMGC (Laboratory of Mechanics and Civil Engineering, Montpeliier), where we carry out research (experimental and modeling) on the biophysical determinism
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contribute to data analysis, feature engineering, model development, evaluation, and documentation, while progressively gaining exposure to production systems, client-facing work, and modern AI practices
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position activities: conducting research using 3D models, including organoids; participating in the design and implementation of in vivo studies, as well as collection and processing of biological material
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framework to model complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal scales than conventional electronic structure methods