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https://www.academictransfer.com/en/jobs/360069/postdoc-inverse-design-of-broad… Requirements Additional Information Website for additional job details https://www.academictransfer.com/360069/ Work
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 1 hour ago
collaborate closely with Professor Colin Meyer at Dartmouth College to develop new models of polynya dynamics, efficient algorithms for inversion of surface signatures, and deeper understanding of controls
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. The PDRA will quantify the differences in calculated and measured experimental conditions by adapting the Geodetic Bayesian Inversion Software ( https://doi.org/10.1029/2018GC007585) ). Working alongside our
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variability. The work may include inverse problems, regularization strategies, statistical modeling, representation learning, and geometric or variational approaches to volumetric data. There is substantial
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11 Mar 2026 Job Information Organisation/Company Grenoble INP - Institute of Engineering Department Engineering Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions
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potentially geophysics (inversion) to develop new exploration targeting methods. These will be designed to make the most of the excellent outcrop conditions in Saudi Arabia, which means that a particular focus
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Do you want to advance inverse modeling for continuous‑ and discrete‑time systems? We
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7 Mar 2026 Job Information Organisation/Company CNRS Department Géosciences Montpellier Research Field Geosciences Astronomy Environmental science Researcher Profile Recognised Researcher (R2
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, signal processing and inverse modelling. The role is based at the Image X Institute and will be led by Dr Chandrima Sengupta. You will benefit from a strong mentoring environment; lead and be part of high
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modeling of structural variability. The work may include inverse problems, regularization strategies, statistical modeling, representation learning, and geometric or variational approaches to volumetric data