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in the following fields: Machine Learning / Pattern Recognition Machine Learning / Generative AI Machine Learning / Pattern Recognition Machine Learning and Pattern Recognition are subareas of AI aimed
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of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant for the new green steels compositions, including impurities and tramp elements. These models should enable
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spoken and written. Additional experience that would be considered a plus: some experience in programming and the use of computer algebra in algebraic geometry; experience with noncommutative algebraic
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algebraic geometry, or representation theory; familiarity with programming and the use of computer algebra. Our offer A position for 18 months, with an extension to a total of four years upon successful
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; experience with AI and machine learning methods, especially in the areas of natural language processing or graph neural networks; the ability to work independently and collaboratively in an interdisciplinary
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, Philosophy, Educational Sciences, and Health Sciences. Through our bachelor’s and master’s degrees, Professional Learning & Development programmes, and interdisciplinary research themes – including Emerging
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Machine Learning Problems > Constantly questions finance/trading data and stays motivated to seek answers despite most often proving that there is no correlation or signal > Experience in setup of research
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investigate how machine-learning based algorithms can be used to personalize the user experience. The goal of this personalized user experience is to enable each individual user to discover their own
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required. Experience with the design, development and verification of TT&C and PDT subsystems for space applications is required. Very good knowledge of modern computer systems, simulation and modelling
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testing. Expertise with analogue electronics design, computer-aided design (CAD) or electromagnetic simulation is an asset, as is experience of working on projects and in large teams. Knowledge and