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Deadline: 30 June 2025 Details This project aims to develop new algorithms for reinforcement learning from human feedback, to effectively solve complex reinforcement learning tasks without a predefined
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the results of which would be used to enrich the available experimental data in order to develop a Design for Manufacture and Performance concept based on machine learning algorithms where the required
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argon. The analysis of the ProtoDUNE data will help to validate calibration techniques and particle identification algorithms. The candidate should have a good knowledge of particle physics and experience
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, including how to guarantee the properties of stability and constraint satisfaction while probing the system and learning a new model. This project aims to develop novel algorithms for the adaptive distributed
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electromagnetic design. We will explore advanced topologies for mmwave metasurfaces, design novel reconfiguration mechanisms, and develop intelligent algorithms to optimize scattering characteristics in real-time
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on specific use cases, including multi-modality perception systems, to enable testing and validation of robotic manipulation strategies. Implement intelligent algorithms for the robotic execution
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processing, data analysis, data-driven modelling, optimisation and computation algorithms, machine learning models and neural network structures, as well as strong skills and experiences in computational
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decision with multiple data sources. One example is to develop the semi-supervised methods and dynamic system interfacing algorithms to produce an automated and real-time information exchange across
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hardware-in-the-loop (HiL) techniques and ML algorithms for the accurate and on-time detection of faults, so that failures can be prevented by alerting the end-users and diagnosticians during periodical
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. Assess the build quality of parts generated through control model algorithms. Validate that methodologies developed are transferrable between different LPBF platforms through evaluation of parts generated