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. The application must be in English and include a curriculum vitae, degree certificate, a complete list of publications, a statement of future research plans and information about research activities, teaching
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a good knowledge of statistical mechanics, good English communication skills and experience with all-atom or coarse-grained molecular simulations. The candidate should be comfortable with programming
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multidisciplinary team environment. Further, we will prefer candidates with some of the following qualifications: Solid background in programming using Python (PyTorch, TensorFlow), R or other languages. Experience
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multidisciplinary team environment. Further, we will prefer candidates with some of the following qualifications: Solid background in programming using Python (PyTorch, TensorFlow), R or other languages. Experience
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. The application must include: Cover letter (max. 1 page) Project description and research plan (max. 4 pages) Curriculum Vitae with applicant's e-mail and phone number Documentation of qualifications (examination
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-level programming, developing prototype implementations, and evaluating their research in testbed experiments. An interest in generating real-world impact and collaborating with the industry. Excellent
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: PhD in computer science, machine learning, operations research, transportation engineering or a related field. Programming skills in C/C++ and Python, along with experience working with simulation
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, electrical engineering, communication engineering, computer science, or a related field. Documented experience with deep learning techniques (e.g., CNNs, Transformers) Strong programming skills in Python and
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, degree certificate, a complete list of publications, a statement of future research plans and information about research activities, teaching portfolio and verified information on previous teaching
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grades according to the European ECTS system obtained in BA and MSc programs. Complete list of publications, indicating which publications are most relevant for the position. Brief research plan linking