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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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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
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algebra, inverse problems, signal processing, and machine learning. The position requires close collaboration with experts in both image processing and imaging physics, as well as with researchers in
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5 Jan 2026 Job Information Organisation/Company Czech Geological Survey Department Center for Lithospheric Research Research Field Environmental science » Earth science Researcher Profile First
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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
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wave solvers, experience with tomographic inversion problems, and experience with image reconstruction methods for photoacoustic computed tomography and ultrasound tomography. Job Description Primary
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of physics- informed machine learning and deep learning, with applications to inverse problems in scientific imaging and the modeling of complex physical systems. The overall goal is to integrate the knowledge
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in data analysis or modeling. Experience with inverse modeling approaches. Required documents: CV with a clause: "I consent to the processing of my personal data contained in my job application for
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processing and inversion techniques to experimental data from different regions and link the findings to relevant processes of the soil-plant system. For further information visit our website http://www.fz
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, computational physics, computational materials science, inverse problems, signal processing, x-ray science etc. are encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be