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- Delft University of Technology (TU Delft); Published 11 Nov ’25
- Delft University of Technology (TU Delft); yesterday published
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deep learning models with state-of-the-art algorithms based on histology whole slide images. They may also contribute to research of integration of multimodality data with clinical, radiological images
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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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-cell and spatial-omics research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic
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algorithms Predicting structured output Self-supervised learning Computational metabolomics Computational biomedicine Computational drug discovery Excellent technical and communications skills are required
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, the successful candidate will conduct cutting-edge research in: Adaptive incentive mechanism design for complex sociotechnical systems Strategic learning and equilibrium-seeking algorithms in transportation
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” and “wet” lab workflows). You will be able to Design, develop and implement algorithms and systems based on foundation models, large language models and/or AI agents for automated scientific discovery
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. The participant may also have the opportunity to be involved with the development of novel algorithms, bioinformatic tools or analytical pipelines that quantify the diversity of RNA viruses that may be deployed in
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Regularization. We aim to develop mathematical understanding of implicit regularisation properties in deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical