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Field
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/Python and signals processing Understanding of electromagnetics Experience with CAD and mechanical design How to apply: Interested candidates should submit a full formal application, guidance and the
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. Applicants should also demonstrate: High academic achievement in relevant undergraduate and graduate courses Proficiency in programming (C/C++, Python) and experience with ROS Practical experience in hardware
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the professional field. (punctuation: 30) Item 02: Specific SW and HW merits: (1) Proven experience in programming with software development languages such as C, VHDL, Python, etc., in aerospace and defense
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, laser physics) Fundamental programming skills and expertise, e.g., Python, Julia, C/C++ Willingness to work independently and contribute to lidar soundings during nights/weekends in accordance with
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for admission to the faculty's Doctoral Programme . Good programming skills in one or several languages (e.g., Python, Julia, MATLAB, Fortran, C++). Competence in mathematical modeling and languages
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of working in a multidisciplinary, international consortium?Are you familiar with Python, MATLAB, or similar tools for data analysis and optimization?Are you eager to contribute to EU-wide goals on energy
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, biochemistry, or other relevant discipline Excellent written and spoken English skills High degree of independence and commitment Good knowledge of python and C++ Experience in relevant work tasks are
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they will investigate novel Cloud and Edge techniques that are built based on Java, Python and C++. The particular research topics of interest include• application development• orchestration and usage
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the possibility of linkage to administrative registers. The role of the candidate will focus on developing reproducible code (Stata, R, Python) to curate raw accelerometer data and derive metrics that characterize
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | 2 days ago
, or publications. Programming/engineering expertise. Proficient in Python and practical with a deep-learning framework (PyTorch or TensorFlow). Comfortable writing clean, reproducible code and using version control