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), are open to applicants worldwide, regardless of residency or domicile. Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral
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://doi.org/10.1016/j.clinbiomech.2021.105424) S. Bozkurt, A. Borghi, L. S. van de Lande, N.U.O. Jeelani, D. J. Dunaway, S. Schievano, “Computational Modeling of Patient-Specific Spring Assisted Lambdoid
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- contributing to the net-zero target’, in The Role of 6G and Beyond on the Road to Net-Zero Carbon, M. A. Imran, A. Taha, S. Ansari, M. Usman, and Q. H. Abbasi, Eds., Institution of Engineering and Technology
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combines engineering with gastrointestinal health and the team are looking for a candidate with a background in engineering, chemistry, biochemistry or related science. The project is sponsored and supported
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-related challenges cost an estimated £4 trillion annually, highlighting an urgent need for innovative solutions across industries. This project aims to transform antifouling technology by developing a new
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Manufacturing (AFM2) group, offering the successful candidate access to a world-class research environment. The project provides an exceptional opportunity to develop expertise in materials science, computational
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Summary A Collaborative project between Geography and Environmental Sciences and Fine Art. The objective of this research is to broaden the knowledge base that helps to develop an understanding how
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models, the approach reduces the need for manually creating features from data, which is typically labour-intensive. Using LLMs in this way has challenges, such as high computational demands, but
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or domicile. Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part
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Analysis using Physiological Signals). The objectives will investigate three core challenges set out in a recent review on Multimodal Emotion Recognition [2]: (1) feature engineering of high dimensional data