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. Antonio Scialdone’s group at Helmholtz Munich, a leading European hub for AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models
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partner from data sciences provides data management and AI based Image analysis, an internal simulations group working on quantitative models to reproduce and predict experimental data, and an internal
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, Python, and SAS or Stata) • Demonstrated expertise in causal inference and high-dimensional risk adjustment/predictive modeling, experience with Medicare claims data • Clear scientific writing and
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of the ocean models. This analysis is fundamental to understanding and ultimately enhancing the skill of coupled ocean-atmosphere models used for tropical cyclone and hurricane prediction. The incumbent is
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We are seeking a highly motivated postdoctoral researcher with expertise in battery safety, thermal management, and/or computational modeling. This position integrates rigorous experimental
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, and clinical data. - Apply machine learning and foundational modeling to support predictive or exploratory analyses. - Collaborate with interdisciplinary teams to refine multi-modal pipelines
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have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models for Alzheimerâ™s, Parkinson, and Dystonia as well as to identify novel
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have extensive knowledge on processes governing cross-shore transport and can use experimental data to develop predictive models. Experiences within numerical modelling of coastal processes is considered
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. Investigates treatment-associated toxicities using clinical data to develop predictive risk models. Applies survival analysis techniques to evaluate long-term outcomes in diverse pediatric patient populations
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variants as functional and assess their impact on gene expression. Contribute to large-scale modeling of engineered traits to predict performance and optimize design. Required Qualifications: PhD in