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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
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, Micro-C/Hi-C, BS-Seq/EM-Seq), massively parallel enhancer assays (ATAC-STARR-seq), and comparative/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle
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of cybersecurity and AI, i.e., attacks and defenses leveraging AI solutions, or attacks and defenses within AI solutions (e.g., backdooring, model poisoning, membership inference), cybersecurity of generative AI
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, membership inference), cybersecurity of generative AI / LLMs; Cyber-physical systems security, e.g., in the fields of robotics, industrial plants, smart grids, automotive, drones, underwater robotics
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline Experience in causal inference, decision-making, or reinforcement learning research
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by combining all available data, taking advantage of the varying temporal resolution and different time spans that the records cover. This work will involve Bayesian tools developed by our research
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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dynamics for shape change. A further aspect of the project is learning and calibrating these models from data using data-driven inference methods. Who we are looking for Required qualifications A doctoral
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appropriate treatment—ultimately saving lives. We are particularly looking for applicants with experience in prediction models and biomarker evaluation, causal inference, longitudinal methods, survival analysis