23 development-"https:"-"https:"-"https:"-"Foundation-for-Research-and-Technology-Hellas" PhD positions at University of Warwick
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About the project: Developing a Theory of the Magnetic Fingerprint of Stress in Materials Supervisor: Dr Chris Patrick, University of Warwick In the development of sustainable materials and
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substantial hurdles for storage, transmissibility, and long-term curation. This PhD project aims to address these challenges by researching and developing specialized lossless and lossy compression methods
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. This PhD project aims to create advanced XCT workflows by developing Artificial Intelligence (AI) and Machine Learning (ML) tools to support imaging before the reconstruction phase. The research will focus
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understanding of the hot workability and oxidation behaviour of ruthenium alloys is a major barrier to their commercial product development. This aim of this project is to gain a fundamental understanding of the
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early detection and predict adverse pregnancy outcomes. You will develop and validate a data-driven clinical decision support tool in collaboration with clinicians and industry partners. Pre-eclampsia is
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reliable transmission of demanding multi-modal data such as haptic feedback, video, and 3D sensing data. This project will develop AI-driven predictive network intelligence to anticipate delay and network
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(such as demand spikes) can threaten the power grid stability. The PhD project will identify and develop solutions to mitigate power grid instability caused by AI data center loads, ensuring resilient grid
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: Imagine a surgeon operating remotely through a robot—what if the network slows at a critical moment? Even tiny delays can risk patient safety. This PhD project develops new AI approaches to predict network
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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions
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), where heat is stored/released through reversible chemical reactions. This project focuses on NaOH water TCES systems, which use cheap, abundant materials [1]. We will develop modelling tools that combine