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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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Mathematics, Computer Vision, or Data Science. -Knowledge of statistical inference methods and machine learning. -Experience in spectroscopy and imaging is an asset. -Strong programming skills in Python
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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skills for this position are: o Good knowledge of the technological challenges of agricultural/viticultural robotics. o Proven skills in: vision-based robot modeling and control; computer vision and
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scienceEducation LevelPhD or equivalent Skills/Qualifications PhD in computer science Background in probability, Markov chains, MDPs Knowledge about reinforcement learning and planning are a plus but not necessary