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of interacting particle methods for Bayesian inversion by including model error in the likelihood evaluation. As model problem, we will consider the inference of parameters in phenomenological models for cardiac
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++, or Fortran). Interest in network science, causal inference, or system identification. Track record of peer-reviewed publications. Ability to work independently and collaboratively. Curiosity, motivation, and
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development and statistical modelling in resilience assessment (e.g., dynamic/latent-variable models, Bayesian hierarchical models, causal inference, time-series analysis, cognitive modelling) Build robust
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The Institute of Medical Biology Chinese Academy of Medical Sciences (IMBCAMS) has launched the global recruitment program of tenured/tenure-track professors and outstanding postdoctoral researchers
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. Responsibilities include processing large-scale sequencing data, developing and benchmarking methods for splicing and regulatory network inference, integrating multimodal data with clinical information
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causal inference frameworks that link genetic variants to cellular mechanisms and therapeutic opportunities. Our research spans immune biology, cardiac disease, neurodegeneration, and cancer, unified by a
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, machine learning, and causal inference frameworks that link genetic variants to cellular mechanisms and therapeutic opportunities. Our research spans immune biology, cardiac disease, neurodegeneration, and
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independently and as part of a team Preferred Qualifications Experience in graph-based AI models, multi-omics data integration, or network inference Background in epigenomics, gene regulation, or aging biology
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influenced corrosion (MIC) in marine environments. It uses AI-supported models, Bayesian data fusion, and real-time sensor data integration. Your responsibilities include: Development of a digital twin (DT
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, paleobiology, geoecology, evolutionary research, or a related field Experience in computational modeling, analysis of ecological or evolutionary data, or causal inference Proficiency in model calibration