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-inspired, method-driven theory to application-driven research. Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms
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University of Darmstadt seeks candidates at the earliest possible date for two Professorships for Algorithmic Graph Theory and Logic. The positions are limited to a period of six years and come without
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of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim or http://github.com
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 29 days ago
the information they contain. This project, part of the Helmholtz School for Marine Data Science (MarDATA), will develop new data science methods to improve the interpretation of biodiversity patterns from long
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and automata, and the interaction between formal methods and artificial intelligence. We welcome candidates interested in topics ranging from foundational theory to the development and evaluation
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. Your task in detail: Design a data pipeline to extract and preprocess activity-relevant features from monitored building power consumption data Develop a classification algorithm (e.g., based on Gaussian
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reduced teaching responsibilities. We are a federal competence center for AI education and research. Our research pushes the boundaries in numerous aspects of intelligence, including algorithms and models
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develop advanced models, algorithms, and control solutions for simulating, optimizing, and operating future integrated energy systems. We address the challenges arising from the increasing integration
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specific quantum algorithms. We focus working on interfaces between disciplines in large innovative projects that strive for the development of scalable and useful quantum computers. In a tight integration
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exciting opportunity for you to join our team working on Scientific Machine Learning (SciML). The project focuses on developing scalable HPC algorithms and mixed-precision solvers to train neural models