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, transfer learning, federated learning, data integration, algorithmic fairness, survival analysis, and methods for heterogeneous and multi-source data. Training Environment and Career Development
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in the Phi_Lab, led by Dr. Azeem Ahmad, and will focus on the development of advanced reconstruction algorithms and next-generation quantitative optical microscopy and tomography systems for imaging
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, the CSATLab , our SW Simulators , and our Facilities . For further information, you may refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . Your role Develop innovative methods and data-driven AI tools
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look forward to receiving your application! Do you have a background in machine learning and interested in telecommunications? You have a chance to contribute to development of sensing methods for new
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the activities of GT3. The initial phases will focus on studying the ideal frameworks for creating the IT platform and developing AI algorithms for data analysis. In particular, the data storage structure will be
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 10 days ago
candidate will develop independent and collaborative research in the area of Machine Learning, Natural Language Processing, and Algorithm Design, and work in a diverse cross-disciplinary team with researchers
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postdoctoral fellows to perform cutting-edge research in AI for radiation therapy. Research areas include developing and implementing AI techniques for image-guided radiation therapy, such as image
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the development of efficient algorithms and codes for multilinear algebra, with a particular focus on the use of innovative parallel programming models and tools. In the context of this task and as part of the Exa
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of this thesis focus on several key areas: - Detection and modeling of node mobility: Supervised learning models will be developed to identify mobility patterns based on radio indicators such as RSSI, SNR, and
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical