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, including: Robot Learning: Creating algorithms that empower robots to learn autonomously from interactions and adjust to new tasks. Manipulation: Enhancing techniques for precise and adaptable object handling
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Learning, Algorithms, Noise Handling (Error Correction/Mitigation), and Verification. These roles are part of the Quantum Software Lab (QSL, link: https://www.quantumsoftwarelab.com ), in collaboration with
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algorithms. The research focuses on wind energy applications, creating a compelling sustainability narrative: developing more efficient computational methods to optimize wind farm performance, which in turn
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aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will support the development
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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis
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Experience in devising and developing novel machine learning algorithms Hands on experience with ROS and physical robots Excellent mathematics skills, particularly in areas relevant to robotics and AI
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-disciplinary research environment Desirable criteria 1. Experience in devising and developing novel machine learning algorithms 2. Hands on experience with ROS and physical robots 3. Excellent
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dynamics, solid mechanics, soft matter or active matter. • To become familiar with simulation algorithms as needed, assist in the development of new ones, test and document any newly developed
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A position exists for a Post Doctoral Research Associate in Department of Applied Mathematics and Theoretical Physics to work on the theory and implementation of algorithms and protocols on quantum
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developing new algorithmic approaches for TAPS data, interpreting the results in the context of phenotypic observations, and communicating these findings clearly to the broader team. You will prepare the