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related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm and experience in using these algorithm for electrical engineering problems
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, operating systems, programming languages, formal methods, real-time systems, security and cryptography, and theory of computation and algorithms. In addition, members of the Department collaborate closely
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model characterisation strategies definition of the necessary equipment definition of the necessary processing algorithms Implementation/validation/drafting of scientific articles in collaboration with
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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum
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pipeline to evaluate the accuracy of deep learning algorithms. Apply deep learning models to analyze large-scale molecular and cellular datasets related to high risk model of schizophrenia generated in our
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for synthesis. A postdoctoral position is available for 3-years to work with Professor Florian Hollfelder at the Biochemistry Department of Cambridge University (https://hollfelder.bioc.cam.ac.uk/ ). The project
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mechanics/molecular mechanics (QM/MM) calculations are used to examine chemical reactions occurred in enzymes. Furthermore, we develop novel simulation algorithms and new models for biomolecules to overcome
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-leading, diverse and UK-wide research programme in probabilistic AI. The hub will develop the next generation of mathematically-rigorous, scalable and uncertainty-aware AI algorithms. This will be achieved
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/Programming skills are essential to complete this project through geospatial modeling automation, image fusion algorithm development with advanced STARFM process, quality peer-reviewed publications, etc
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, develop theory and algorithms for their practical use, and study complexity and performance trade-offs in relevant applications. The project is led by Professor Erik Agrell (IEEE Fellow), whose