Bachelor of Research Training Group 2088 — Discovering Structure in Complex Data: Statistics Meets Optimisation and Inverse Problems at University of Göttingen
    Go to University of Göttingen

    Bachelor of Research Training Group 2088 — Discovering Structure in Complex Data: Statistics Meets Optimisation and Inverse Problems at University of Göttingen

    University of Göttingen
    University of Göttingen
    Germany
    Germany, Munich
    University Rank:#243
    0
    Bachelor of Research Training Group 2088 — Discovering Structure in Complex Data: Statistics Meets Optimisation and Inverse Problems at University of Göttingen

    Study Research Training Group 2088 — Discovering Structure in Complex Data: Statistics Meets Optimisation and Inverse Problems at University of Göttingen, a Bachelor degree offered on the Göttingen campus in Munich, Germany. This Full Time program is delivered on campus in English and takes approximately 3 years to complete. Students benefit from a structured learning experience designed to build both theoretical knowledge and practical experience.

    About Bachelor of Research Training Group 2088 — Discovering Structure in Complex Data: Statistics Meets Optimisation and Inverse Problems

    The efficient extraction of significant information from complex data is one of the grand challenges in applied sciences. Continuously growing capacities for the acquisition and storage of large data sets call for new application-adapted approaches to process data efficiently and to extract the embedded information. The ability to detect relevant structural information is often hampered by the complexity of data sets as well as by noisy and indirect measurements. The Research Training Group (RTG) 2088 focuses on new mathematical concepts for the efficient reconstruction and classification of relevant structural information in data sets without reconstructing the entire information inherent in the data. One of the guiding principles of this RTG consists of discovering and rigorously exploiting structural a priori information in order to obtain the desired information. We aim at utilising a wide range of a priori knowledge - such as topological structures, probability metrics, sparsity in adaptive dictionaries, or natural non-quadratic bending energies - to design numerically and statistically stable and efficient algorithms for the recovery and classification of information. Methodologically, we focus on an interplay between approaches in statistics, optimisation, and inverse problems. Important solution concepts include generalised regularisation techniques, multi-scale methods in harmonic analysis and statistics, statistical inference for topological structures, non-linear local and global spectral dimensionality reduction, and cutting-edge, iterative algorithms at the interface of statistics and optimisation.

    Requirements and Admission Criteria for Bachelor of Research Training Group 2088 — Discovering Structure in Complex Data: Statistics Meets Optimisation and Inverse Problems at University of Göttingen

    Entry Requirements

    Master's degree or equivalent in mathematics or a similar field

    Tuition and Fee Information for Bachelor of Research Training Group 2088 — Discovering Structure in Complex Data: Statistics Meets Optimisation and Inverse Problems at University of Göttingen

    How to Apply for Bachelor of Research Training Group 2088 — Discovering Structure in Complex Data: Statistics Meets Optimisation and Inverse Problems at University of Göttingen

    Bachelor of Research Training Group 2088 — Discovering Structure in Complex Data: Statistics Meets Optimisation and Inverse Problems at University of Göttingen
    University of Göttingen
    University of Göttingen
    Germany

    Germany, Munich

    WhatsApp student consultation with YouApply