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. Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems. Experience with geospatial analysis, spatial data integration
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) or discrete manufacturing (e.g., electronics assembly, automotive, home appliances). Explore the integration of large language models and reinforcement learning for real-time optimization, fault self
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datasets [e.g. behaviour, simultaneous EEG-fMRI and eye-tracking data]. Main research themes include, but not limited to: reinforcement learning and valuation, risk and uncertainty, confidence and
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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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reinforcement learning and/or tensor networks are preferred. Other responsibilities include communicating results in journal publications and conferences as well as mentoring junior members of the group
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human well being mutually reinforce one another. We endeavor to foster an inclusive, collaborative work environment to bring interdisciplinary expertise to solve critical environmental problems
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. Methodological Areas of Interest Applicants with experience in the following areas are especially encouraged to apply: Optimization (deterministic, stochastic, robust, reinforcement learning–based) Systems
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, reinforcement learning, and/or wireless communications. The candidate should hold a doctoral degree in electrical or computer engineering or related fields. The successful candidate will have a strong publication
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of CADRE and the Business Analytics Department. Desirable Qualifications: Expertise in the specific areas of reinforcement learning and/or interpretable machine learning is highly desired. Experience in use