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specimens to estimate historical age structures over the last 150 years. Forecasting Shifts in the Pollination Service Window. The researcher will use Bayesian inference (e.g., Integrated Nested Laplace
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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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causal inference with structural economic modelling. Subject area The subject area for this position is economics. Fields of specialization: Labour Economics – AI and the Economy; Econometrics – Structural
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dynamics and behaviour, e.g. aerial/drone surveys, line transects, camera surveillance and photo-ID. Experience with Bayesian statistical modelling Proven ability to handle large ecological datasets and
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dynamics and behaviour, e.g. aerial/drone surveys, line transects, camera surveillance and photo-ID. Experience with Bayesian statistical modelling Proven ability to handle large ecological datasets and
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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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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