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Position Description The successful candidate will work on using satellite observations of atmospheric methane to better quantify methane emissions on regional to global scales through inverse analyses
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to address more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
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Chemical Engineering and will contribute to the digital tool development within two EPSRC funded projects: IDEA: Inverse Design of Electrochemical Interfaces with Explainable AI (grant number EP/W03722X/1
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Chemical Engineering and will contribute to the digital tool development within two EPSRC funded projects: IDEA: Inverse Design of Electrochemical Interfaces with Explainable AI (grant number EP/W03722X/1
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Position Description The successful candidate will work on using satellite observations of atmospheric methane to better quantify methane emissions on regional to global scales through inverse analyses
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28 Oct 2025 Job Information Organisation/Company UNIVERSITY OF SURREY Research Field Computer science Engineering Chemistry Engineering Physics Researcher Profile First Stage Researcher (R1) Country
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18 Sep 2025 Job Information Organisation/Company LINGNAN UNIVERSITY Research Field Computer science Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country Hong
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Position Description The successful candidate will work on using satellite observations of atmospheric methane to better quantify methane emissions on regional to global scales through inverse analyses
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to the development of Bayesian inference frameworks that use GATES. The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling frameworks to estimate
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to the development of Bayesian inference frameworks that use GATES. What will you be doing? The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling