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of Physics and Astronomy. AIPAD tackles the above questions by developing two innovative AI algorithms: The first algorithm will infer full SEP pitch-angle distributions (PADs) for spacecraft measurements
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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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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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: 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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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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experimental materials synthesis, characterisation or both, and optionally, with experience developing algorithms for accelerated discovery based on data collection from automated instruments. Practical