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the Unconventional Communications and Computing Laboratory (UC2), led by Dr Michael T. Barros, which develops modelling and algorithmic methods for networked communication and computation under real-world constraints
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turbine blades. Successful re-development for end-of-life composites could enable reuse in other structural applications. This PhD will investigate the development of hierarchical Bayesian algorithms
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on methodological development in cryo-electron microscopy (cryo-EM), particularly in image reconstruction and 3D volumetric analysis of macromolecular structures. Rather than aiming to incrementally optimize existing
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co
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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research
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intelligent sensing, followed by detection of the important events.In the light of autonomous decision making, the project aims at developing machine learning algorithms for knowledge extraction from data
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developing prototype systems and algorithms • Experience with MRI and/or fMRI data (human data preferred) • Experience with high resolution and high field MRI and/or fMRI • Excellent verbal and written
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Intelligence This project works with leading researchers in the Faculty of Pharmacy to develop new artificial intelligence technologies to aide discovery of drugs to treat pharmacoresistant epilepsy. You can
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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Center for Biologics Evaluation and Research (CBER) | Silver Spring, Maryland | United States | 2 days ago
of synonymous variants in therapeutic proteins using integrated computational and experimental approaches. A central component of the project involves applying and developing advanced bioinformatic tools to model