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Department of Data Science

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      • Analytics & Mixed-Integer Optimization
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Department of Data Science

  1. Home
  2. Research
  3. Research Groups
  4. Optimization under Uncertainty & Data Analysis
  5. PhD Theses

PhD Theses

In page navigation: Research
  • Agile Teams
    • IBO – Intelligent Business Optimization
    • DynDat – Dynamic data inversion, learning and control
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    • Analytics & Mixed-Integer Optimization
      • Group members
        • Dr. Andreas Bärmann
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        • M.Sc. Kristin Braun
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        • M.Sc. Patrick Gemander
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        • Prof. Dr. Yiannis Giannakopoulos
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        • M.Sc. Lukas Hager
        • M.Sc. Katrin Halbig
        • M.Sc. Lukas Hümbs
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        • M.Sc. Jan Krause
        • M.Sc. Vanessa Krebs
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        • M.Sc. Richard Krug
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        • M.Sc. Alexander Müller
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        • M.Sc. Oskar Schneider
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        • M.Sc. Dennis Adelhütte
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        • M.Sc. Kevin-Martin Aigner
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        • M.Sc. Christian Biefel
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        • M.Sc. Jana Dienstbier
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        • M.Sc. Lukas Glomb
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        • M.Sc. Benno Hoch
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        • Dipl.-Math. Manu Kapolke
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        • M.Sc. Martina Kuchlbauer
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        • Prof. Dr. Frauke Liers
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        • M.Sc. Florian Rösel
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        • Sebastian Tschuppik
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    • Partial Differential Equations
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        • Munib Agha
        • Prof. Dr. Frank Duzaar
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    • Stochastic Methods in Data Science
  • Professors, Lecturers and Researchers
    • Frank Duzaar
    • Yiannis Giannakopoulos
    • Martin Gugat
    • Jens Habermann
    • Jan Heiland
    • Johannes Helbig
    • Manfred Kronz
    • Frauke Liers
    • Alexander Martin
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    • Dieter Weninger
    • Marius Yamakou
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  • Secondary members of DDS
    • Freimut Bodendorf
    • Günter Leugering

PhD Theses

PhD Theses

PhD Theses treat usually complex mathematical optimization problems, for which there exist no satisfying solution methods so far. The problem results usually from concrete applications in industry and economy or from engineering and scientific questions.
The main interest of our working group lies in optimization problems which contain combinatorial elements or decision-demanding questions, i.e. questions, which permit only yes/no answers. Mathematically, such questions can be formulated as mixed-integer linear or nonlinear problems (short MIP or MINLP). A thesis usually contains the setting of a suitable mathematical model, the analysis of the underlying MIPs and/or MINLPs, the development and extension of suitable solution procedures as well as the application on real and realistic data.
Visit the joint website with group Analytics & Mixed-Integer Optimization for an overview of current and previous works.

Department of Data Science
Friedrich-Alexander-Universität Erlangen-Nürnberg

Cauerstr. 11
91058 Erlangen
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