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Qualifications Strong background in electrical engineering and computer engineering. Hands-on experience in computer networking and embedded systems. Proficiency in programming using Python and C/C++ Proficiency
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: Experience with generative AI deep learning and active involvement in data science and machine learning projects. Experience in neural network architecture, cloud computing, and database development and
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of tensor networks to quantum and classical reservoir computing Additionally, the candidate will have the opportunity to collaborate with experimental groups at the Jožef Stefan Institute (IJS) working
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depending on funding. The Oxford Ion Trap Quantum Computing group currently hosts one of the world’s highest performance networked quantum computing demonstrators, capable of remote Bell-pair production
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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programme designated to attract and empower the world’s brightest early-career researchers. This fellowship supports outstanding scientists who harness Artificial Intelligence (AI) to accelerate breakthroughs
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the delivery of clean energy and industrial decarbonization infrastructure associated with net-zero transitions. The role will report to the Andlinger Center's Dr. Chris Greig, the Theodora D. '78 and William H
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We are seeking a Research Fellow to perform research on deployment of machine-learned models for health analytics on distributed IoT/edge/cloud systems using transprecise computing and contribute
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coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UK Power Networks, SSE Energy Solutions, and the University of East London, you will develop robust economic Model
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Quantitative Genetics, Statistical Genomics, Computational Biology, Plant Breeding, or a related field. Strong expertise in GWAS and post-GWAS analysis (e.g., fine mapping, gene network modelling, GWAS boosting