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- NTNU - Norwegian University of Science and Technology
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knowledge of MATLAB/Python and signals processing Understanding of electromagnetics Genuine interest in battery technologies Experience with CAD and mechanical design How to apply: Interested candidates
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, or computer science. Core competencies: solid background in quantum many-body physics strong programming skills (Python required, Rust a plus) experience with tensor networks, variational Monte-Carlo, machine learning
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with experience applying these models to practical applications for real-world challenges. Good programming skills, Python is mandatory, other languages (e.g., Java, C++) will be considered a plus to
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Python and/or Julia using LINUX-based OS. Communication of findings through journal publications, conference presentations and project reports. Candidate Requirements Applicants must satisfy the criteria
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, specifically with Python-based engineering software development and hardware interfacing. Excellent oral and written English skills Demonstrated ability to independently conduct engineering projects Demonstrated
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validating mechatronic systems. Experimental methods and testing Programming and software development, specifically with Python-based engineering software development and hardware interfacing. Excellent oral
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. Ability to develop, understand, and critically evaluate machine learning research software, preferably using Python and PyTorch. An interest in foundation models, self supervised learning, multimodal
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have: MSc in engineering or similar discipline by the start date of the position Experience with mechanical modeling and simulation Experience in computer programming/scripting (e.g., C++, Python, Matlab
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programming skills are a plus: MATLAB, Python, Git Fluency in English An open personality and good communication skills in written and spoken English. TU Delft (Delft University of Technology) Working at TU
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technologies in NLP, LLMs, and RAG. Programming skills in Python, including experience with machine learning libraries (e.g., PyTorch, Scikit-learn, Hugging Face Transformers) and development tools (e.g., Co