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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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the project is the development of AI-based pipelines for detecting, segmenting, and classifying lichen communities. Convolutional neural networks (e.g., U-Net, DeepLab) and machine-learning algorithms (e.g
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learning and machine learning for biological data Sequence and structure analysis of large-scale datasets Functional annotation and evolutionary analysis Collaborative research with experimental virology
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for robotics, perception, prediction Computer vision and data acquisition System modelling and control JUNIOR RESEARCHER – Responsibilities Participate in R&D tasks across ongoing projects Support experimental
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027
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the following ones. Exploration of active auditing techniques for large machine learning models, use of reinforcement learning, potential application to recommender systems. The PhD will mainly investigate
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nano-computed tomography, and scanning electron microscopy design and implement deep learning models to enhance resolution of large field-of-view imaging techniques integrate imaging data across