Marie Skłodowska-Curie Actions

Expression of interest (EoI) - Marie Sklodowska-Curie Action -Image and signal processing (ISP) group -University of Valencia

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    15/07/2019 17:00 - Europe/Brussels
    H2020 / Marie Skłodowska-Curie Actions
    Spain, Paterna
    University of Valencia
    Image Processing Laboratory (IPL)
    Image and signal processing (ISP) group

The ISP group at the develops machine learning data analysis techniques and vision algorithms. We focus on methods able to extract knowledge from empirical data drawn by sensory (mostly imaging) physical systems. These measurements depend on the properties of the scenes and the physics of the acquisition process. Our approach to signal, image, and vision processing combines machine learning theory with the understanding of the underlying physics and biological vision.

Applications mainly focus on computational visual neuroscience, image processing, remote sensing data analysis, and geosciences. The problems posed in these disciplines require similar mathematical tools, where model inversion, uncertainty estimation, and causal inference from empirical data play a central role. You are invited to follow our activities in http://isp.uv.es or live in Twitter, https://twitter.com/isp_uv_es

Two important research lines are pushed now in our group: (1) physics-aware (domain-knowledge) machine learning, and (2) observational causal inference from observational data, mainly spatio-temporal Earth data. While the first line requires expertise in machine learning (probabilistic modelling, deep learning methods and statistical analysis. The applications mainly involved problems in the Earth sciences (geosciences, climate science and remote sensing). The group is very interdisciplinary, so the requested researcher’s profile should own PhD in statistics, maths, physics, or computer science, and ideally the candidate should have a solid background on Earth and Climate sciences.

Research field: Computer science, Mathematics, Environmental sciences, Geosciences, Physics, Agricultural sciences.

Documents to be submitted by the candidates: Brief 2-pages CV including the list of publications


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