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Field
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mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. Experience in applying or developing machine learning
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differences or random direction stochastic approximations, offer general-purpose strategies that only rely on function evaluations. While conceptually simple, zeroth-order methods tend to scale poorly with
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Location: Odense, 5230, Denmark [map ] Subject Areas: Pure math with relations to quantum theory or with emphasis on Quantum algorithms, Quantum software and Quantum computing. Appl Deadline: 2025/12/01 11
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. The task of the theory group led by Prof Kyriienko at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will
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corresponding knowledge in another way. A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms
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diagnosis, and therapy of diseases like cardiovascular diseases or cancer. Overall, the institute strives to advance precision medicine by combining knowledge from different fields such as biology, chemistry
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for project documentation, setting up the work packages, and oversite of the clinical trials. You will take the lead on internal communications making sure that members of the different work packages are kept
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computational algorithms to enable regenerative cell therapies. Now, we are seeking a highly driven postdoctoral researcher to contribute to our ambitious mission. Division The Division of Biomolecular and
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and algorithm or approaches, and instead will have a creative approach to problem solving, exploring multiple potential techniques. Ideas will be verified on software systems (e.g. SOLIDWORKS, MATLAB
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architectures Analysis and preprocessing of different data types (texts, images, time series, graphs, etc.) Review latest literature and data Orchestration and documentation of deep learning experiments