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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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imaging and machine learning. The main task of the successful candidate will be to help redefine certain traditional criteria of comparative anatomy used in archaeozoology and to establish new criteria
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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning
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continuous programme improvement. Participate in educational initiatives and activities to enhance student learning outcomes. Requirements: A PhD or a Master’s degree (with significant industry experience) in
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biological environments - Experience using machine‑learning algorithms for luminescence signal analysis and sensing applications - Experience writing scientific articles and presenting results at conferences
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, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
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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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Energy Storage! Fully funded 4-year PhD Studentship in Chemistry - Machine Learning-Accelerated Quantum Chemical Modelling of Molecular Junctions and Surface Catalytic Reactions PhD Studentship: Taking
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Aalto University is inviting applications for a Postdoctoral researcher in molecular machine learning. The successful applicant will join the research group of Professor Juho Rousu. The position
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools