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optimize high-throughput crystallographic workflows for AI-readiness. · Ensure structural outputs are suitable for training and benchmarking ML models. · Contribute to the development of the Fragalysis
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, and optimize algorithms for contact-based mobile manipulation. Apply reinforcement learning, imitation learning, and motion / manipulation skill learning to real robot tasks. Integrate learned policies
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into the same devices. The research project is part of a larger consortium, gathering world-class researchers in remote sensing with expertise ranging from estimation and optimization theory to hardware design
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whilst ensuring optimal workstation performance. Please refer to the job description for details on the essential criteria required for this role. ADDITIONAL INFORMATION Annual leave entitlement is 28 days
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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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compared to those produced via hand layup. Selecting the optimal combination of prepreg, reinforcement, and film to suit the complexity of a specific component prior to production trials is another critical
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will focus on three interconnected challenges: 1) Novel Inverse Reinforcement Learning (IRL) for Optimal Stopping Traditional IRL methods are not designed for noisy, trajectory-based optimal stopping
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economics, computer science or a related field. You are familiar with methods for optimization under uncertainty, in particular decision-focused learning. You are an experienced programmer, preferably using
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, and high-performance computing for biotechnological applications. The project aims to develop computational tools for bioprocess optimization, combining modeling, simulation, and data-driven methods
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position aims to conduct holistic modelling and analysis of integrated energy systems to reach optimal system performance while incorporating various sustainable energy infrastructures. Potential research