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Field
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precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related
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and mapping, light fields, extended reality (XR) technologies, sim-to-real, synthetic data generation, and advanced computer vision and machine learning techniques. In addition, the group works on
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complex interaction patterns that may carry important biological information. By integrating deep learning, genome-wide simulations, functional genomics, and large-scale biobank data, AI:GENOMIX aims
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, including machine learning approaches to transcribing, re-structuring and analysing archival collections. The PhD student employed will be part of an award-winning team of computational social historians
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techniques employed include high-throughput cell based screening, flow cytometry and preclinical treatment models. Informatic approaches include data mining and manipulation of large pharmacogenomic matrices
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student in this project, you will contribute to the development of new models and methods in machine learning for D-MIMO integrated sensing. This includes working with large amounts of data generated by a
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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geolocated social media data, and computational techniques from network science and machine learning. It is interdisciplinary, combining theories of healthy and accessible cities with computational data
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically
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candidate will perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon