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) with publication list, Verified copy of doctoral degree certificate or documentation that clarifies when the degree of doctor is expected to be obtained, Verified copies of certificates from higher
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, use imitation learning algorithms to learn pick-and-place actions, design HRI experiments with users, evaluate data, and share the code and benchmarks in open repositories. This postdoctoral position is
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to develop novel AI and machine-learning methods for accelerated materials discovery. The focus is on combining generative AI, active learning, first-principles simulations, and machine-learning potentials
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modelling, geospatial data science, generative modelling, mobility data analysis, or transport simulation, especially when combined with knowledge of complex systems, network science, resilience theory, urban
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motivation for applying for the position. · Curriculum vitae (CV) with publication list (including a brief description of the applicant’s contribution to each publication). · Verified copy
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-precision hardware without compromising simulation fidelity. Data-centric optimization: Designing and implementing communication schemes and data access strategies targeting post-exascale systems, e.g., using
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, solid state transformer, DC/DC converters, inverters, etc. Experience in modeling and stability analysis Experience in hardware development or real-time simulation. Awareness of diversity and equal
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letter summarizing your qualifications, scientific interest, and why you are applying for the position (max 1 page), Curriculum vitae (CV) with publication list, Copy of doctoral degree certificate
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for implementation in finite element simulations of material failure. These models will be applied to study the ductile-to-brittle transition in HSLA steels. Qualifications Requirements A doctoral degree or an
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letter (max two pages, font size 12) Contact details for at least two references. Copy of PhD diploma (alternatively, a letter confirming that the PhD diploma will soon be obtained) and possibly other