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probes) as well as in modern data analysis, machine learning/artificial intelligence, and data science methods is highly desirable. Active engagement in research collaborations within the research division
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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. The Leibniz-LSB@TUM comprises a unique and world-leading research profile at the interface of Food Chemistry and Biology, Chemosensors and Technology, and Bioinformatics and Machine Learning. Leibniz-LSB@TUM’s
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 30 days ago
enabler of machine learning for eDNA-based assessments of deep-sea ecosystems” (m/f/d) Background Deep-sea ecosystems host highly diverse biological communities that provide key ecosystem functions and
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also influenced by the intestinal microbiome and its metabolite, especially in the cancer treatment areas such as immune checkpoint blockade or CAR-T cell therapy. The Research Division "Microbiome and
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, multimodal modelling) focusing on the complete spectrum of human communication channels. It aims to understand how women and men remember about their time in Nazi concentration camps, as well as the way memory
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tasks Your job tasks includes the following responsibilities: Structural optimization and calculation of optical spectra of novel ternary metal nitrides using density functional theory and machine
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vision models Experience with event-based cameras, neuromorphic vision concepts, spiking neural networks, and/or neuromorphic computing is a plus Experience with, or willingness to learn, ROS 2 for robotic
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) to design representations and transferable energy models for proteins and materials. Contribution to teaching on statistical physics and machine learning. The position will serve to develop your own
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scientists. We seek to appoint an expert in the research area of Machine Learning for Sustainable Processes and Materials with a focus on data-driven methods for modeling, analyzing, and optimizing complex