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Field
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. Candidates will perform research on agentic AI, foundational modeling, optimization, and control of multiagent autonomous systems with an application in renewable energy and power grids, in addition to working
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developing optimized modules in C#/C++ within Unity and/or Unreal Engine Experience with database management systems For network engineer role: Experience in computer networking, multiplayer architecture, and
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the project leader in the research project - “Ascend-HPR: A highly efficient ascend architecture-based first-order mathematical optimization solver”. Qualifications Applicants for the Senior Research Fellow
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fellow with a Ph.D. in electrical engineering, applied mathematics, or related field. Candidates will perform research on agentic AI, foundational modeling, optimization, and control of multiagent
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://research.nottingham.edu.cn/en/persons/jie-yu About the Project. This research project aims to explore innovative strategies for green supply chain management and carbon footprint optimization in the context of dual carbon
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inspection system that are fabricated by robots and multimodal sensors. Development of analytic models, simulation, and optimization for the quality inspection systems. Development of a predictive model for
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effects of urban greenery and develop a modelling platform to assess and optimize the cooling benefits of various types and configurations of urban greenery in Singapore Key Responsibilities • Conduct multi
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the mentorship of leading experts in one of the following priority research areas: Research area 1: Intelligent Structural Optimization using Physics-Informed Reinforcement Learning Research area 2: AI-Enhanced
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models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees. Research areas include Representation Learning, Machine learning and Optimization
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Optimized Tissue Culture and Single-Cell Omics Digital Composite Manufacturing Multi-Element Alloys Resistant to Hydrogen Graph based inverse reinforcement learning (IRL) framework for analyzing and