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. Key Responsibilities Search and synthesize literature; draft methods/results sections and appendices. Prepare slide decks, posters, and stakeholder summaries as directed. Maintain accurate lab notebooks
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forecasting. Familiarity with ensemble methods, Bayesian approaches, and uncertainty estimation. Experience with large-scale or messy real-world data (structured and/or unstructured). Interest in or experience
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 18 days ago
the data payload size and lead to inefficient resource use. Consequently, grant-free methods [1] that eliminate or reduce control traffic are well suited for these scenarios. Removing coordination
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by developing AI methods that improve recognition of rare species while providing reliable measures of uncertainty. Using state-of-the-art computer vision approaches — vision transformers, self
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Administrative Guide, http://adminguide.stanford.edu/ . The expected pay range for this position is $85,376 to $99,581 per annum. Stanford University provides pay ranges representing its good faith estimate
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4 Nov 2025 Job Information Organisation/Company The University of Manchester Department Computer Science Research Field Computer science » Computer systems Researcher Profile First Stage Researcher
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Administrative Guide, http://adminguide.stanford.edu . The expected pay range for this position is $115,103 to $134,261 per annum. Stanford University provides pay ranges representing its good faith estimate
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datasets Apply modern causal ML methods (such as Double ML, BLP, GATES, CLAN) to estimate treatment effects and related quantities of interest Use cross-fitting, resampling, and multiple model specifications
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of computer codes. Specific Requirements Educational Requirements: Knowledge of mathematical and computational modeling with partial differential equations and the finite element method. Scientific programming
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes