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Field
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characterization of deep-water habitats, GIS spatial analysis of species distribution data, and quantification of ecosystem services. Preference will be given to applicants that possess a diverse set of skills and
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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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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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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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-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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, 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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Professor that will be capable of contributing to multiple ongoing research projects in the lab. Potential projects include, but are not limited to, oceanographic characterization of deep-water habitats, GIS
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through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a