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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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Learning for Foundation Models’, where the aim is to adapt these models to new tasks without forgetting previous knowledge. The precise focus of the project can be defined in collaboration with
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biomarker data. Develop predictive models and algorithms to identify risk factors, disease markers, and potential therapeutic targets for Alzheimer’s disease. Implement machine learning models to improve
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organisms respond to hypoxic stress and nervous system function using the model organism C. elegans. The individual will report directly to the laboratory Principle Investigator (PI). Position Status Full
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mathematics and learn more about modeling of atmospheric or oceanic flows, or the motion of charged fluids such as plasmas? We are looking for a Doctoral student to become part of Klas Modin's group
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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at conferences, and stakeholder engagement sessions. Required Qualifications: A Ph.D. in Climate Science, Hydrology, Environmental Science, or a related field. Experience in machine learning or AI applications in
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machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi