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. A formal training, education, or certification in a secondary area (beyond the main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning, artificial intelligence
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of substitution models using large dataset, successful applicants must then have a PhD and demonstrated experience in discrete choice models, machine learning techniques, big data, and optimization
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-edge machine learning, including Large Language Models (LLMs), to enhance decision-making and planning in robotic systems. Qualifications: Applicants must have a PhD in Robotics, Control Theory
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in Multiphysics modeling in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications
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must hold a PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field, with a strong publication record in AI-driven energy solutions. Proficiency in machine learning
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. Kyriakopoulos seeks to improve the autonomy of Field Robotic systems by fusing control theoretic and machine intelligence approaches. Formal models are directly applied in real experimental facilities. Marine
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, and Simultaneous Localization and Mapping (SLAM) is desired. The position is open to PhDs with background in robotics, controls, AI, and/or computer vision. The candidate is expected to work in a highly
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Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a
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must have a PhD in Robotics, Control Theory, Mechanical or Electrical Engineering, Applied Mathematics, or a closely related field, with a strong focus on robot control, machine learning, and
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development (R&D) of advanced machine learning (ML) models like Transformers, Vision Transformers, Large Language Models (LLMs) and other advanced deep learning models for vision-based applications. The focus