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an ambitious research program. It will utilize a data-driven approach to support decision-making for an optimal energy system, with specific focus on cost-effectiveness, emission reduction, and social
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personalized. The candidate will investigate textiles, computational design, and functional validation of hand-worn assistive devices, aiming to create textile exoskeletons that can conform to complex hand
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, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
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modelling tools (CST or HFSS) - Fabricate and test for optimal electromagnetic performance, such as bandwidth, return loss, insertion loss and power-handling. - Develop and characterize new bonding/alignment
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 20 days ago
. The research will be computational based, and at this stage is still broad, so we can formulate the optimal plan for the right candidate. We will take an interdisciplinary approach, and you will be able
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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05.06.2025, Wissenschaftliches Personal Are you looking for an opportunity to shape the future of quantum computing? With superconducting quantum computers on the verge, we aim to strengthen our
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clocks and laser links between Earth and the international space station ISS. The concrete task is to optimize our strontium lattice clock (DOI: 10.1103/PhysRevA.98.053443), transfer it to the Geodetic
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production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current bottlenecks in data and
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become the bottleneck in achieving optimal performance and trustworthiness. This project will focus on how a federated multi-task learning framework can be effectively designed and optimised to address