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transitions and universality for spectral statistics of random matrices and their applications in high-dimensional statistics, machine learning and probability theory. The Department of Mathematics at KTH
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machine learning models in simple, standalone devices that are capable of advanced processing. Building on our work on solution-based neuromorphic classifiers (https://doi.org/10.1002/advs.202207023
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. Teaching may also be included, but up to no more than 20% of working hours. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose
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statistics, unsupervised machine learning, optimisation, model predictive control. Experience in financial mathematics. Having high integrity, be process-oriented and able to work independently. Being able
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strategies: Leveraging traditional and causal machine learning approaches to determine which patients are most likely to benefit from specific therapies. Digital pathology and image-based analyses (starting
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consists of 18 research groups covering a wide range of mathematical disciplines – from pure and applied mathematics to numerical analysis and optimization, as well as mathematical statistics and machine
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of the following areas: state models, time series analysis, computational statistics, unsupervised machine learning, optimisation, model predictive control. Experience in financial mathematics. Having high integrity
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scientific curiosity Mastery of data visualization and scientific communication Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently
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and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research project The postdoc will work at Chalmers University of Technology in a
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Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently Teaching and mentorship abilities or interests in personal development A