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environment-specific algorithms and machine learning approaches. At the end of the project a technology demonstrator will be built using UAV- and USV- mounted radar sensors, and it will be tested in real world
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efficient peptide catalysts for powerful C-C-bond forming reactions. (2) Prof. Francesca Grisoni (https://molecularmachinelearning.com/ ) leads the Molecular Machine Learning Group at the Technical University
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architectures in the field of computer vision and with training, validating and inference processes in machine learning; Familiarity with generative AI; Curious about mathematics and biology; Excellent
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mechanics at the atomic scale. In this project, the University of Groningen will develop an array of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant
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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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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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We are looking for a doctoral candidate with a strong computational, engineering, data scientific or machine learning background that is keen to work in an interdisciplinary environment and open to
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also be able to demonstrate excellent ability to code with or learn computer programming languages, such as C++, C#, Python, and/or Matlab. A desire to engage in cross-disciplinary research
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Worldwide Prof David Wagg Application Deadline: 30 April 2025 Details We are seeking an enthusiastic and self-motivated PhD student to join a collaborative project funded by EPSRC, working on an innovative
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under the “Cryptographic elements of trustworthy AI” project. The main research objectives for the project are the following: Analyze security of Machine Learning (ML) models against data modifications