216 machine-learning-"https:"-"https:"-"https:"-"https:"-"RAEGE-Az" positions in Switzerland
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methods and approaches are needed to better tackle the challenges posed by increased uncertainty and complexity. Machine learning (ML) and artificial intelligence (AI) methods have shown promise
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& Machine Learning: Experience in deploying machine learning models and data science workflows in a research context (e.g., cheminformatics, predictive modelling). Design of Experiments (DoE): Knowledge
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-read sequencing data analysis is highly desirable. Familiarity with signal processing or applied machine learning is advantageous. You should demonstrate strong motivation to develop innovative
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dynamical systems, and machine learning, with applications to synthetic biology and biomolecular circuit design. Our research develops mathematical and computational frameworks for understanding and
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of cutting-edge tools, models, and strategies to understand and engineer immune systems for translational medicine. Candidates may use integrative approaches that combine immunogenomics, machine learning
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Systems.”Funded through an ETH Zurich Career Seed Award, this project aims to develop scientific machine learning frameworks that integrate physics-based modeling with neural network architectures. The goal
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knowledge and technology from research to Swiss machine, electrical and metal industries. The research group Control and Automation at inspire AG offers the following position in collaboration with Bota
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
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thinking with a structured, quality-focused approach to data and methods. Ideally, experience in one or more of the following: data engineering, building data-driven apps, computational linguistics, machine