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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment. This project will involve applying and evaluating statistical and machine learning models for data
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data, thermophysical data and modelling approaches Knowledge/interest on data-driven approaches, i.e. machine learning Experience and knowledge in sorbent-based CO2 capture Experience of interaction
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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comprehensive knowledge graph for SSH and integrating applications based on artificial intelligence and large language models, GRAPHIA will convert heterogeneous SSH data into more interoperable and machine
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these short-lived events are notoriously hard to reconstruct and to model, so our understanding of their behaviour during warmer climates is limited. To learn from past warmer climates and better understand
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inference, and Machine Learning methods. In addition to leading their own research projects, the appointed candidate will have the opportunity to contribute to the projects of PhD students in the group, as
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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characteristics. Nominate and help evaluate promoter regions and candidate genes to enhance nitrogen use efficiency. Apply machine learning models to classify molecular variants as functional and assess
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include: (1) implementing light–matter interaction in CFD via the radiation transport equation and suitable attenuation models; (2) integrating kMC-based surface kinetics through machine-learning surrogate