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in AI and machine learning – from classical approaches to large language models. You are proficient in Python and key ML libraries (e.g. scikit-learn, PyTorch, LLM APIs), and you have a track record of
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Python with demonstrable familiarity with PyTorch, experience in working on shared codebases, excellent applied math skills (especially probability theory, matrix algebra, calculus). Beyond technical
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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and scored: A. Knowledge of Matlab, Python, R and/or C++ (advanced level). These will be assessed based on accredited training hours (0.5 points for every 10 hours of training, up to 5 points for each
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Matlab, Python, R and/or C++ (advanced level). These will be assessed based on accredited training hours (0.5 points for every 10 hours of training, up to 5 points for each course) and accredited project
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Experience Experience developing research software using appropriate languages and environements (Python, Julia, Matlab) Knowledge of optimisation problem formulations and solution methods Experience of risk
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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to optimization algorithms Other closely related topics Your profile Master's degree in computer science, physics or a related field Advanced knowledge in the field of Quantum Computing Knowledge of Python Good
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. System Modeling: Develop and refine photonic system models incorporating key quantum mechanisms. Use simulation tools (Lumerical, Synopsys, Matlab, Python, …) to validate architectural feasibility and
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• Understanding of agricultural production systems in the U.S • Experience working with spatial data and machine learning models. • Strong knowledge of programming languages, such as Python, R . • Demonstrated