220 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"University-of-Chester" positions in Switzerland
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80%-100%, Zurich, fixed-term We are looking for a Research Engineer to join ongoing and future research projects at the intersection of machine learning, and structural design (e.g. trusses, space
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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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of this programme. The profile PhD in computer vision, computational biology, physics or a related discipline Demonstrated expertise in image analysis and working with large-scale imaging datasets Strong expertise in
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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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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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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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foundations of conversational AI as well as domain knowledge on how and why it can be deployed effectively. You should have knowledge and skills in both data analytics (e.g., machine learning, statistics
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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