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University Abu Dhabi invites applicants to apply for the open Post-doctoral Associate position to perform primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control
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IF or CORE A*/A conference paper. Robust machine control assumes modeling of robot-environment interactions. An example may include an outdoor autonomous ground robot that needs to be aware of its
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comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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to this research line, planning protocols, overseeing data collection, facilitating communication between teams, and ensuring ethical and regulatory compliance. Implement data-analysis models to predict cognitive
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simulations of compact binaries (including, for example, binary black holes, binary neutron stars, and black hole–neutron star binaries). The broader goals are to generate accurate predictions for gravitational
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prediction, focusing on efficient edge deployment (e.g., through model pruning, quantization, or TinyML techniques). The embedded system will be designed to perform local inference in real-time, minimizing
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efficiency and reliability. Strong background in data analytics, leveraging insights to drive operational improvements and predictive maintenance. Experience in control strategies and automation, ensuring
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materials to enhance the cell robustness. Work plan The work plan for the PhD thesis will be divided in three main steps: 1) A chemo-mechanical model will be built to predict the crack initiation and
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including forecasting models to predict the expected distribution of pests on the field to landscape scale. The research is expected to make pest forecasts and link them to the existing expertise in crop