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close-to-field conditions, and (ii) a fully autonomous phenotyping robot, Phenomobile.v2+, equipped with a set of sensors (LiDAR, RGB, IR, and Spectrometer) that enable advanced plant measurements
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and application of Large Language Models (LLMs), to join our team working on predictive maintenance solutions. The ideal candidate will have recently completed (or be close to completing) a PhD in
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, participate in grant writing activities, and seek external funding opportunities to support ongoing research projects. Contribute on the group’s activities in processing of sustainable materials, (bio)polymer
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recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs
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biomass into valuable bio-based products and understanding the biological properties of seaweeds, with an emphasis on their potential applications in biotechnology, agriculture, and other bioproducts
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activities in processing of sustainable materials, (bio)polymer composites and their applications. Participate in the supervision of undergraduate, master’s, and doctoral students. Criteria of the candidate
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of UAV demonstrators at UM6P and OCP sites. Methodology: AI Development: Reinforcement learning, computer vision, and sensor fusion for autonomy. Digital Twin & Edge Computing: Implement real-time
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such as air quality monitoring, water leak detection and energy monitoring of electric vehicle batteries. The candidate will need to master sensor technologies, embedded systems, and communication protocols
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and real-time data acquisition systems. Participate in testing and deployment of UAV demonstrators at UM6P and OCP sites. Methodology: AI Development: Reinforcement learning, computer vision, and sensor
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on the effective integration of renewable energy, resource efficiency, and waste reduction. The candidate must hold a PhD in Urban or Rural Development, Civil Engineering or related domain. The candidate is expected