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in Python. Interest in (or prior exposure to) metamaterials / architected materials (e.g., lattices, TPMS, graded microstructures) and homogenisation concepts. Basic understanding of anisotropy and
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validation on simulated and real data Skills Abstraction capabilities, strong programming skills in C/C++ and Python, collaboration skills. English is needed. REFERENCES [0] C. Bekker, S. Rothmann, M. Kloppers
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-authored peer reviewed papers) • Well-developed statistical software skills (preferably in R, Python, GIS) • Competences in quantitative research methods – ideally knowledge of several of the following
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, mathematics, or related scientific disciplines. Skills in numerical tools and programming are desirable (MATLAB, python, C++ etc). Any experience or capabilities in engineering design or manufacturing methods
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) Certificates or other relevant documentation (if applicable) Link(s) to public GitHub repositories or other code portfolios demonstrating relevant programming experience (e.g., in C++, Python, or Java
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for hands-on experimental characterization techniques and data analysis. Skills in programming (e.g., Python, MATLAB) and simulation tools. Expertise in photonic integration is not a must, but having relevant
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with Robotics Excellent programmer in Java / C / Python / ROS or equivalent Excellent at using Machine Learning software, e.g. PyTorch / TensorFlow / Scikit Learn Highly knowledgeable in mathematical
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-theoretic models is a plus; Proficiency in programming (e.g., Python, Matlab, C++); Strong interest in fundamental research on autonomous systems and decision-making. No prior knowledge of contextual radio
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Strong programming skills, particularly in Python Familiarity with knowledge graphs Experience with graph neural network Personal Qualities We value candidates who are: Intellectually curious Self
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skills (Python) and knowledge of deep-learning frameworks (PyTorch) are expected. A certain affinity towards turning complex concepts into real-world practice is desired. The successful candidate is