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knowledge of mathematics (e.g. linear algebra, probability, statistics, stochastic processes) Experience of developing in larger projects While we don’t expect applicants to know biology, applicants
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with coalescent theory from population genetics. The central idea is to replace heuristic discrete denoising schemes with coalescent-inspired stochastic processes, leveraging the deep duality between
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research ideas, and to have solid mathematical foundations, such as in linear algebra and relational algebra, to support the design and analysis of your algorithms. You should be curious, motivated, able
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electrical or computer engineering (or a closely related field), with a solid foundation in mathematics (e.g., calculus and matrix algebra) and coursework in areas such as digital signal processing, sound
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position A new PhD fellowship in path signatures, stochastic analysis, and
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The Master project should treat methodological aspects with mathematical tools. The candidate must show a strong interest in method development. Proven competence in probability, linear algebra, and
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of possible scenarios but also increases the uncertainty on post processing conditions, constrains and expected results. This research will produce a systematic classification of different alternative scenarios
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/ TensorFlow / Scikit Learn Highly knowledgeable in mathematical and statistical concepts Solid foundation in mathematics for Machine Learning (Linear Algebra, Probability, Optimization). Proficient in English