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practical experience in at least one programming language (preferably Python) Ideally, some practical experience in material characterization methods Structured and analytical thinking as well as a systematic
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, computer science, or a related field with an overall grade of at least “gut” (or equivalent, e.g., cum laude) Expertise in quantum mechanics, experience with HPC, programming (e.g., Python, C / C++), and/or
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with shallow water equations). Python coding for workflow control, data pre- and post-processing as well as model calibration and validation. High-performance computing (HPC) for running test cases and
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computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and
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to submit the dissertation after 3 years and 9 months of research. Desired requirements specific to this project include: experience with a range of relevant computer programming languages such as Python, R
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Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with
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are expected. Knowledge in parallel programming is desirable. Prior knowledge in differential-algebraic equations, Gaussian processes or kernel based methods is a plus; programming experience in Python or C/C
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language (Python/Matlab/C++ etc.) Proficiency in English, both written and spoken Ability to work independently and to work as part of a team Good reasons to join: Look forward to a unique working
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: university and, if applicable, PhD degree (e.g. Master/Diploma) in mathematics, physics, materials science or related subjects basic knowledge of computer programming (e.g. Python, Matlab and C++) excellent
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(e.g. Julia, Python, R) are a requirement Good programming skills in a low level language (e.g. C/C++, Fortran) will be considered advantageous Our offer A vibrant research community in an open, diverse