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machine learning processing of the spectroscopic data • The optical design and development of novel custom spectroscopic sensors benefitting from freeform optics. • Integration of the in-situ
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, Data Science, Machine Learning, or a related field. Experience and skills · Strong knowledge of AI, Machine Learning, data-science (e.g., neural networks, deep learning, autoencoders, GANs, active
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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Disse), the Chair of Geoinformatics (Prof. Thomas H. Kolbe), and the Chair of Algorithmic Machine Learning & Explainable AI (Prof. Stefan Bauer). The project aims to develop an integrated urban flood
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twins, energy islands, electrolyzers, and machine learning. Our team of 26 members from 13 different nationalities values diversity and includes experts in a broad range of scientific disciplines
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of computer vision and machine learning Proficiency in English (oral and written) Experience with Deep Learning is a plus To Apply: Please send a long CV, motivation letter, and academic transcripts to Prof
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calculations Knowledge about machine learning application in condensed matter Knowledge about magnetism, superconductivity, and topological order Personal characteristics We are looking for a candidate who is
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sequencing and researching disease in patient cohorts, working with machine learning techniques and programming computers. The candidate will learn about different flavors of metagenomic sequencing, how