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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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, the project proposes to also use machine learning techniques to learn parts of the prior and penalty structure from data in an interpretable way. Examples include mapping liquidity and volatility features to a
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RPTU University Kaiserslautern-Landau • | Kaiserslautern, Rheinland Pfalz | Germany | about 11 hours ago
communication tailored to their needs. For further information, see: https://www.physik.uni-kl.de/oscar/ Course organisation During the research work, the PhD student has the possibility to participate in
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for their stakeholders and society at large through our MBA, MS, PhD, and Executive Education programs. We are equally committed to cultivating new scholars and teachers and to creating and disseminating pathbreaking
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application! We are looking for a research engineer within the Division of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science. In this position, you will have the
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in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports. You are suited for this position
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Apply and key information This project is funded by: Department for the Economy (DfE) Summary This PhD project offers an exciting opportunity to develop next-generation biocomposites made from
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networking and computer security, and genuine interest in the PhD project. We value a collaborative attitude and an interest in working both in teams and independently. Self-motivation, attention to detail
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requirements Required qualification Applicants must hold a degree corresponding to a Norwegian doctoral degree in biostatistics, statistics, machine learning, mathematics, data science, computer
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the Job related to staff position within a Research Infrastructure? No Offer Description Postdoc in Machine Learned Semiconductor Material Properties for Quantum Transport Simulations The simulation