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Computer Science, Artificial Intelligence, Software Engineering, or a related field. Strong programming proficiency in Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch
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and oral communication skills Proficiency in data analysis tools (e.g., R, Stata, Python, NVivo, GIS) Experience with interdisciplinary or community-based research is a plus How to apply: Interested
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learning, or a related field Strong background in deep learning and statistical analysis Proficiency in Python, R, and deep learning frameworks (e.g. PyTorch, TensorFlow) Strong written and verbal
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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strong track record in programming, preferably in Python. The candidate should have demonstrable expertise in computational modelling of solids, preferably using density functional theory methods. Previous
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interest and solid foundational knowledge in at least one of the research projects Proven competence in computational design workflows, particularly in Rhino and Grasshopper Knowledge of Python, C#, finite
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academic background in machine learning and convex optimization. Prior experience in federated learning, edge computing, or healthcare applications is highly desirable. Proficient in Python and ML frameworks
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closely related quantitative field. • Have experience using statistical software (R, Python, SPSS, Stata), regression analysis, and statistical modelling are required. Preference will be given
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programming skills in at least one of the following (Python, MATLAB, Julia). Please ensure you read the Job Description and Person Specification for full details of this role, by clicking the 'Apply' button
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data analysis on Matlab. • Developing and using models to characterise the soft robots (both sensor and actuator). • Knowledge of programming (C/C++/python/MATLAB), using a prototyping board like Arduino