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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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, volumetric data analysis, optimization methods, statistical modeling, or machine learning for scientific applications. Prior experience with cryo-EM software frameworks or structural biology data is considered
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, test and measurement methodologies for electronic modules, system engineering, data pre-processing and database indexing/analytics for dashboarding/visualisation, embedding machine learning algorithms
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simulations. Two complementary strategies will be employed: structure-based virtual screening (docking simulations + molecular dynamics) and ligand-based virtual screening (machine learning models). We have
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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workloads including embedding generation, LLM inference, and cognitive search. Develop Snowpark Python transformations, UDFs, and machine-learning features. Implement vectorized storage, model-serving
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systems in the research project, including testing and troubleshooting. Implement and test machine learning models, which may involve data preprocessing, model training, and evaluation. Create and maintain
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, organised researcher who can evidence: A PhD, or equivalent in statistics, machine learning or a closely related discipline, OR near to completion of a PhD. Expert knowledge of statistical inference methods
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leverage state of the art machine learning models (AlphaFold2, RFdiffusion) and multi-omics data integration to guide the rational design and optimization of therapeutic antibodies. Overall, you will have
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cleaning and quality control, supervised and unsupervised machine learning, parametric and nonparametric statistical methods, deploying production models, and assisting with the communication of scientific