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Leedham (colorectal cancer biology), Dan Woodcock (cancer genomics), Helen Byrne (mathematical modelling), and Jens Rittscher (computational pathology and imaging AI), offering a unique opportunity to work
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the research program. Qualifications and terms of appointment: Ph.D. in Mathematics, Computer Science, or a related field. Strong background in machine learning, particularly reinforcement learning. Demonstrated
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Sun Yat-Sen University, School of Mathematics (Zhuhai) Position ID: 1695 -POSTDOC2 [#26601] Position Title: Position Type: Postdoctoral Position Location: Zhuhai, Guangdong, China [map ] Appl
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computer science, mathematics, computer engineering, or relevant technical field First-author peer-reviewed published papers (or under review) Proficient programming experience in Python and libraries (e.g., Pytorch
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Computer Science, Information Theory, Physics or related fields High level of mathematical maturity Experience with topics related to quantum LDPC codes and decoding algorithms, or demonstrated ability and
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Mathematical Science Team in iTHEMS and work (concurrently, if necessary) in one of the following laboratories. RIKEN Quantum aims to promote fundamental science based on quantum computation. Successful
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in the Mathematical Institute (https://www.maths.ox.ac.uk/groups/mathematical-biology/infectious-disease-modelling ). The postdoctoral researchers will develop data-driven mathematical models and
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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. Applicants must also have a strong track record of publications in computational materials science, excellent communication skills, the ability to work collaboratively in a team of scientists with different
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods