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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a strong
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. Machine learning will assist in artifact correction, segmentation, and material classification. By combining experimental imaging, simulation, and data-driven interpretation, this approach will deliver high
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. • Demonstrated industrial-related experience. • For those that hold a PhD, a minimum of 3 years of relevant industrial experience is preferred. Required Knowledge, Skills, and/or Abilities • Ability to teach all
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at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
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at TalTech´s web-page: https://taltech.ee/en/phd-admission The following application documents should be sent to tarmo.soomere@taltech.ee CV Motivation letter Degree certificates as required by the university
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triggered by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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attacks in federated learning. Experience should be demonstrated by participation in projects in this area and scientific publications. Already enrolled in a PhD programme. Minimum requirements: Knowledge
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projects and deliverables May supervise undergraduate students working on the AI/ML projects QUALIFICATIONS PhD (or equivalent) in Machine Learning, Computer Science/Engineering, Biomedical Engineering, or
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through external grants or other mechanisms. External funding is also expected to support student research and experiential learning opportunities. The specific appointment split will be determined based