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operation · Application of artificial intelligence or machine learning in energy or engineering systems 5. Strong programming and modelling skills using relevant tools such as Python, MATLAB
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the development of hierarchical computational materials discovery schemes combining random structure searching, machine learning, atomistic, and density functional theory (DFT) calculations to accurately and
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no.: 4135 Explore and teach at the University of Vienna, where over 7,500 academic minds have found a unique blend of freedom and support. Join us if you're driven by a passion for top-notch international
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sufficiently versatile to rapidly acquire the required capacity. You will have : A PhD in gut microbiology or closely related field Good knowledge of gut modelling and general microbiology Experience in
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design, delivery, and evaluation of a multi-school research study investigating the impact of mathematical storytelling on secondary students’ mathematics learning and related outcomes. Working closely
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developing characterisations of network models and interactions with methods in statistical machine learning. The post holder provides guidance to junior members of the research group including project
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reasons to research and teach at the University of Vienna there is one in particular, which has convinced around 7,500 academic staff members so far. They see themselves as personalities who need space for
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techniques in a fast-paced environment with a strong team focus. This represents a unique opportunity to acquire a strong practical knowledge base in a broad range of highly desirable transgenic biology skills
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Learning, Algorithms, Noise Handling (Error Correction/Mitigation), and Verification. These roles are part of the Quantum Software Lab (QSL, link: https://www.quantumsoftwarelab.com ), in collaboration with
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning, epidemiology