Expired

Experiments on large environmental data using pattern recognition and parameter estimation techniques

  • Description of the research topic

    “Large datasets acquired by various remote sensing campaigns have been successfully applied in many fields, such as change detection and classification. As powerful tools machine learning and deep learning (DL) algorithms have brought new approaches to remotely sensed data analysis. The key objectives of these methods are to develop architectures capable of discovering patterns and relations in raw data, and at the same time, to face the big data challenge.
    In morphometric analysis it is a common assumption that the studied surface follows certain rules, and thus, can be approximated with some simple or relatively simple mathematical functions. Parametrization of identified features enables the comparison of surfaces in geometrical sense. The aim of the research project is application of DL methods in pattern recognition and classification in diverse datasets and for various purposes (e.g. geomorphometry, environmental monitoring, archaeology etc.)
    The successful candidate (m/f) will have a strong background in GIS, solid knowledge in a programming language (e.g., Python) and some coding experience. Completed geoscientific, environmental scientific or remote sensing data analysis project is an asset.”

    Thesis supervisor: Balázs Székely

    Required language skills: English
    Further requirements: A strong background in GIS, solid knowledge in a programming language (e.g., Python) and some coding experience. Completed geoscientific, environmental scientific or remote sensing data analysis project is an asset.

    How to Apply?

    If you are interested apply here:[PhD] Doctoral School of Environmental Sciences – Eötvös Loránd University (elte.hu)

    For more information visite the following website:Doctoral School of Environmental Sciences (elte.hu)

  • Funded: Not Funded

    Master Degree: Required

    Duration: 4 Years

    Full/Part Time: Full Time

    Starting Date: 06 September 2021

    Deadline to Apply: 31 May 2021

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