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project is to develop a high-performance computing framework for mass spectrometry proteomics to enhance efficient processing and interpretation of large datasets using deep learning algorithms and GPU
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they encounter with a label of one of the languages in their repertoire. If they encounter text written in a language they have not seen before, they label it with what their algorithm deems the closest match. The
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developed by the project partners will be based on two key technologies: machine learning algorithms that generate artificial yet realistic data points (synthetic health data) and secure multi-party
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such as earth with different sizes of rocks. 2) Development of advanced control algorithms integrated with force and loss models to prevent overloads and hazardous situations, ensuring operational
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embodied and justice-oriented approaches to datafication literacy, to support human agency and mitigate algorithmic harms. The focus will be on empirical research that uses different design and game-making
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Video Sensorization for Secure Healthcare Monitoring (SViSenS) project, which develops privacy-preserving, AI-driven video-based monitoring technologies for healthcare. Your responsibilities include: You
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: neuromorphic algorithms, machine learning, classifier development, AI programming Key tasks include experimental and/or computational research, collaboration within the project team, publishing results in high
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that are of practical interest. The postdoctoral researcher will contribute to the theoretical foundations of inverse problems involving wave phenomena, develop cutting-edge computational algorithms, and apply
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the A.I. Virtanen Institute for Molecular Sciences at the University of Eastern Finland (Kuopio campus) for a Research Council of Finland–funded project developing a novel MRI method for measuring brain
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concerned with optimal transport for inverse problems. Optimal transport for inverse problems One of the central topics of the research projects is the further development of theory and methods