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Field
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physically realistic datasets for AI training, benchmarking, and validation. The successful candidate will contribute to AI.Grids, a flagship European initiative on the application of Artificial Intelligence
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* Integration in interdisciplinary and multi-institutional networks * Excellent scientific infrastructure and mentoring We are looking for: * Curious mind-set with a strong interest in the physics and engineering
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appointment holding a master’s degree with excellent results in disciplines like Aerospace Engineering, Mechanical Engineering or a comparable field of study • Proven ability to identify and solve problems
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engineering, providing an excellent opportunity to contribute to the development of one of the most promising technologies for long-duration electricity storage. Your tasks will include: Developing reproducible
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which technology and research fields are advancing. Furthermore, you will also extend the indicator beyond novelty toward quality assessment and validate the reliability, robustness, and fairness of LLM
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, chemistry, physics, electrical engineering, mechanical engineering, synthetic biology, material science, computer science, micro- and nanotechnology) from a recognised academic institution and the acceptance
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scientists, software engineers, and experimental researchers on topics including: Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM, TEM, EBSD
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. • Master's degree with excellent grades in computer science, mathematics, physics, engineering, medical informatics, biomedical engineering, or a related field • Very strong undergraduate and graduate record
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evaluation strategies. In close collaboration with chemists, engineers and data scientists, a platform is being developed that combines materials development, process optimisation and machine learning
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 8 hours ago
the SIMPLAIX consortium between the University of Heidelberg, Karlsruhe Institute of Technology, and the Heidelberg Institute for Theoretical Studies. The research will focus on generative models