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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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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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(or biomedical PhD with strong computational skills). Fluency in modern AI/ML and emerging paradigms (agents, explainability). Track record of computational solutions to biological problems. Clear communicator
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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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for inferring signatures of natural selection. The position is supposed to be filled at the earliest possible date and initially funded for 2 years with the possibility for extension. Compensation with all public
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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
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for an ambitious researcher with a strong track record in the synthesis and evaluation of co-electrocatalysts catalysts for solid oxide electrolyser cells. You will work within an interdisciplinary team