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Field
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) Demonstrated ability to use programming languages for data analysis and modeling (e.g., Python, Matlab, R) Experience in acquiring competitive third-party funded research projects (e.g., FWF, OeNB, Horizon
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, linear mixed models, structural equation modeling, multilevel models) You are proficient in using one or more common statistical analysis software packages (R, SPSS, Mplus, Python, etc.) You have a very
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or equivalent High competence in machine learning and robotics Knowledge and experience in computer vision is an advantage Good mathematical and programming skills (Python and C/C++; ROS is an advantage) Good
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or equivalent High competence in machine learning and robotics Knowledge and experience in computer vision is an advantage Good mathematical and programming skills (Python and C/C++; ROS is an advantage) Good
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charging technologies Experience in modelling and simulation using Python Knowledge of optimisation methods, energy systems, or smart charging concepts is advantageous Experience with data analysis and
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. * Strong background in numerical mathematics, modelling and simulation is required. * Knowledge in Inverse Problems. * Experience in programming (e.g., Matlab, C, Python). * A high level of competence in
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, particularly in Python and Java Commitment and initiative Strong teamwork and organizational skills Admission Requirements Completed Master's or Diploma degree in Computer Science, Software Engineering and
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. The position also includes administrative tasks related to teaching and research activities. Your Profile Programming experience in a higher-level programming language, such as Python or Matlab, is required
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languages are strongly typed and memory safe, like Rust, C, or Java. Knowledge of Python is welcome, but not sufficient without skills in other languages. Ability to work with virtualized/experimental setups
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discipline Strong programming skills, with proficiency in Python; experience with scientific computing libraries (NumPy, PyTorch or TensorFlow, scikit-learn, nibabel or similar) Knowledge of MRI acquisition