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understanding and generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research
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vision. Understanding or willingness to learn advanced statistical modeling is a plus Assessment criteria and other qualifications: This is a career development position primarily focused on research
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assessment, programming and machine learning. If so, we encourage you to apply! You will develop exposure and physical vulnerability maps for past and future (1970-2100) and integrate these into a flood risk
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, and Gemini. Ability to work effectively in interdisciplinary and collaborative environments. Required Education: PhD from an accredited college or university in Educational Technology, Learning
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models Foundation models represent a breakthrough in AI, as did the shift from traditional machine learning to deep learning. Numerous models become available in the field of Earth Observation and can be
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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., computational learning theory, expressiveness of transformers and other models, xAI, formal methods) or equivalent; willingness to conduct research on artificial intelligence/machine learning, combined with
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schools, doctoral supervision, and software outputs central to the Centre’s mission. About You You will have, or be close to completion of, a PhD/DPhil in Statistics, Machine Learning, Data Science, or a
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qualifications: PhD in machine learning, statistics, computational biology, applied mathematics, physics, or similar. Eligible candidates may apply before completing their PhD degree. However, a documented proof
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focus on developing play-based learning resources and interdisciplinary multimedia projects, with some aspects overlapping with formal STEM education and policy. In addition to project responsibilities