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modelling the coupling of atmospheric and micro-physics moisture dynamics. The work will be carried out in collaboration with and under the supervision of Professor Edriss S. Titi. Duties include mathematical
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, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for
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Postdoc: Geospatial AI Modelling & Uncertainty Quantification of Biomass Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 32 to 40 Application deadline
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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, Oxford, Leeds, Reading, and Birmingham) and international (Utrecht University, ETH Zurich, Université Catholique de Louvain, etc.) scientists to use new modelling resources and methods to elucidate drivers
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the following areas as well as the will and commitment to learn relevant topics from the other areas: Statistical and machine learning, mathematical and statistical modelling, statistical image analysis and
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learning or applied mathematics. Required skills and qualities: - Fluency with Python programming for data analysis or machine learning, - Knowledge of statistical or probabilistic modelling techniques
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Join our team as a Early-Career PostDoc (m/f/d) in the research area of AI‑based modelling full-time (39.2 hr/week), fixed term for two years. Leibniz-IWT stands for research in the field of new
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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(preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models to the clinic The post holder will be based in the Department