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PhD scholarship in Mid-Infrared Frequency Comb Technology for High-Sensitivity Sensing – DTU Electro
partners. Responsibilities and qualifications Your main focus will be to strengthen the department’s expertise in ultrafast lasers and frequency combs while developing innovative solutions for high
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-flavanol cocoa product developed by an industry partner. Unlike previous research, it enables investigation of dose–response effects within realistic dietary intake. This interdisciplinary PhD spans
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explaining issues clearly or prioritising them by player impact. This collaborative doctorate (University of Surrey, SAHCI, and Sony Interactive Entertainment, SIE) will develop a QoE-driven agentic AI
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this project, you will develop the next generation of federated machine unlearning algorithms—methods that can efficiently deliver genuine, verifiable, and robust erasure without sacrificing model performance
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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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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academic breaks. Full-time, exempt role working 40 hours per week 52 weeks per year. What You Will Do: Develops and delivers instructional material regarding digital scholarship methods and technologies
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such as virtual reality are being used to increase motivation and adherence to exercise programmes without the need for in person contact. This work builds on our existing co-development and implementation
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standard of musicality and potential to further develop their conducting skills. Benefits This award provides a one-off payment of approximately $8000 and a professional development opportunity. Amount: Up
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density