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Intelligence & Machine Learning Develop machine learning and AI models to identify, predict and characterise genome instability patterns. Design generative and predictive computational models to infer mutational
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* Under the guidance of the professor, students will contribute to: Curate and analyze datasets of superionic conductors' structures and properties. Build and train machine learning models (especially graph
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Machine Learning group at TDB and SciLifeLab (Associate Professor Prashant Singh), which develops methods and software for simulation-based inference, generative models and robust machine learning, together
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models. This role is ideal for those who have a demonstrated history in collaborative research, effective communication, the ability to follow standard protocols, and an aptitude for troubleshooting
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identify the cells, cellular states, and biological programs through which genetic variation influences disease. Your work will sit at the intersection of statistical genetics, machine learning, and
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PhD Scholarship Develop AI and machine learning models to guide real-time, personalised treatment of paediatric brain cancer using multiomics and clinical data, within the internationally
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learning. All of our system institutions place strong emphasis on service — helping to build healthier, more educated communities in South Carolina and beyond. Veterans' Preference Statement The University
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international conferences to present your own work. Your Profile Masters degree with subsequent PhD in computer science (or a related field). Rich experience in devising machine learning models, methods, and
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, and commercialization through AI-enabled approaches. The successful candidate will develop, implement, and apply computational, machine learning, cheminformatics, structural modeling, and data science
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven