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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original
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, or behavioral data) and be proficient in Python and modern deep-learning frameworks (ideally PyTorch). Experience in computer vision, multimodal data fusion, self-supervised or generative modeling is highly
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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will develop a new high-precision digital twin for WDS based on a self-calibrated hydraulic model with a machine learning correction model and, together with advanced data analytics, will detect and
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quantitative focus on these fields Solid foundation in statistics and/or machine learning, e.g., supervised learning, regression modeling, model evaluation, or high-dimensional data analysis Good programming
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Professor level. The ideal candidate will be at the forefront of research that integrates modern machine learning methods with economic theory and econometric analysis. We are particularly interested in
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neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building
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paradigms centered on human perception. Finally, the recent rise of foundation models and multimodal artificial intelligence opens up new perspectives at the interface between coding and machine learning
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 12 days ago
creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search, interpretation
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools