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Field
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, geometallurgy or related field Experience in either stochastics, deep learning or minerals processing is needed Structured and solution-oriented working style, analytical thinking and above-average committment
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turbulence, and use this knowledge to identify control strategies through deep reinforcement learning. The methods developed in this project will directly contribute to designing novel porous media
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participate in the creation of next-generation drug discovery platforms for AMD. The successful applicant should have research experience in at least two of (a) deep learning and AI methods; (b) causal search
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and development Generative AI models for sound, music, visuals, 3D graphics, or movement Projects related to Generative AI Background in mathematics and statistics of Deep Learning What you will do
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-of-the-art facilities and deep expertise. Our infrastructure spans the full spectrum from fundamental electrochemical studies to the fabrication of lab-scale components and devices. Responsibilities and
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
relationships, together with deep domain expertise. These methods open new possibilities for extracting and connecting knowledge at scale. The goal is to enhance digital twins with the capability to interpret
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collaboration with Q.ANT GmbH in Stuttgart, a deep-tech company that develops photonic computing and photonic sensing products. The goal of this project is the development of highly integrated vapor cells with
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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deep learning methods to enhance the predictions beyond existing data. By incorporating microstructural features into predictive models, the aim is to create a reliable data-driven modelling framework