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Position as Postdoctoral Research Fellow available at the Department of Biosciences as part of the UiO: Life Science Convergence Environment AUTORHYTHM. The candidate will develop new machine learning
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the underlying molecular mechanisms behind ion conduction inside MOFs. The overall goal of the research project is: To develop and apply machine learning potentials (MLP) to model, understand, and optimize proton
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machine learning techniques and statistical analyses, including Cox Proportional Hazard models and polygenic hazard scoring, to fit and validate prediction models; (2) perform quality control and imputation
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systems or other related fields (e.g., sociology, anthropology, history, textual studies, digital humanities, design, human-computer interaction, art history, visual culture, artistic research
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systems or other related fields (e.g., sociology, anthropology, history, textual studies, digital humanities, design, human-computer interaction, art history, visual culture, artistic research
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interaction, art history, visual culture, artistic research). The Postdoctoral Research Fellow will be expected to devote their time to their own research agenda as well as working with a dynamic team of
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demonstrates sufficient computing and data analysis skills to exploit Run-3 data. Fluent oral and written communication skills in English. Desired qualifications: Knowledge of and experience with machine
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well as machine learning/artificial intelligence and robotics. Excellent skills in written and oral English The following are also desirable: Skills in psychology-inspired computing models, user-centered design
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machine learning techniques Interest or experience in statistical modelling and analysis Interest in mathematics and natural science Experience with scientific publication and conducting research Experience
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experience with machine learning as applied to high energy particle physics. Some experience in software development and distributed computing. Personal skills: Ability to work independently and take