Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
. 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
-
27 Dec 2025 Job Information Organisation/Company LINGNAN UNIVERSITY Research Field Computer science Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country Hong
-
leaders to develop and promote human-centric technology and social policies. Further information about Lingnan University is available at https://www.ln.edu.hk/ . Applications are now invited for
-
5 Jan 2026 Job Information Organisation/Company Czech Geological Survey Department Center for Lithospheric Research Research Field Environmental science » Earth science Researcher Profile First
-
scientific imaging (TR-FLIM, HSI, ISM), designing mathematical and unsupervised learning algorithms for nonlinear inverse problems, with reliable reconstructions even with limited data. Where to apply Website
-
Processes, Diffusion models, Flow Matching) and their applications to Bayesian inverse problems, and Literature review around constrained generative modeling or sampling/inference. Usage of GP models as an
-
, optimization, or inverse modeling Familiarity with wearable or implantable systems for diagnostic or therapeutic use Background in multimodal sensing integration combining acoustic, optical, or mechanical
-
Skills (see guide $...): Common foundation of management and coordination skills Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR6034-CECLEP-001/Default.aspx Work Location(s) Number
-
of inverse problems, Bayesian learning, and uncertainty quantification. The specific project will be tailored to your expertise and interests; examples include: Efficient inference techniques for high
-
join the research group of Jan Glaubitz and develop your own research agenda in the context of the group’s research at the intersection of inverse problems, Bayesian learning, and uncertainty