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for diagnosis and prognosis of different sarcomas. He/she will develop and train deep learning models with state-of-the-art algorithms based on histology whole slide images. They may also contribute to research
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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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(LES) results. Key Responsibilities: Develop and refine numerical algorithms for real-time wind field forecasting. Validate forecasting models against high-fidelity LES data and field measurements
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during surgery or endoscopic exploration. This postdoctoral position aims at developing innovative deep learning algorithms to help histology classification. Both classical histology based on hematoxylin
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focuses on modeling IoT-Fog environments, designing multi-objective optimization algorithms (latency, energy, reliability), and developing strategies for critical IoT applications like smart cities and
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algorithms for ultrasound simulation, imaging, and quantitative analysis Customize machine learning / deep learning methods for image reconstruction Conduct human studies of the algorithms and techniques in
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algorithms for ultrasound simulation, imaging, and quantitative analysis Customize machine learning / deep learning methods for image reconstruction Conduct human studies of the algorithms and techniques in
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reconstruction to overcome these challenges. Your tasks - develop physics-informed, self-supervised learning approaches for phase retrieval - implement reconstruction algorithms on HPC clusters for large-scale
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models were successfully developed with methods of Quantum Machine Learning. In cooperation with the University partners the aim of this project ls to translate classical analysis and simulation algorithms
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integrating artificial intelligence, from algorithm design to on-sky demonstration. The objective is to design intelligent adaptive optics systems capable of correcting sensor nonlinearities, anticipating