volume: full/ part time
restriction: 3 years
salary: EG 13 TV-L
beginning: 01.01.2023

The Fluid Mechanics Group is searching for

PhD candidate in machine learning 

applications in turbulent convection (m/f/d)

Your tasks

The research project MesoComp, which is funded by the European Research Council (ERC), is focused to an improvement of our fundamental understanding of the dynamics and origin of turbulent superstructures – an order at the intermediate mesoscale. Examples for such pattern formation processes in highly turbulent flows are found at the solar surface or in atmospheric convection. The research is focused to numerical simulation studies of turbulent mesoscale convection in compressible fluids in combination with a comprehensive analysis of the large-scale structure and the statistical properties of the flow. Scalable models of the convective turbulence and its transport properties are developed on the basis of classical and quantum machine learning algorithms that can reduce and parametrize the complex dynamics effectively. The interdisciplinary research in the MesoComp project is at the interface of turbulence research, applied mathematics, machine learning, and quantum computing. The tasks comprise one or more of the following points,

  • analysis of the statistical properties of the turbulent convection flows in view to the turbulent transport of heat and the universality of statistical laws,
  • development of recurrent and generative machine learning algorithms to describe the flows in reduced-order models and to obtain small-scale parametrizations,
  • porting of classical machine learning algorithms on quantum computing devices.

The PhD candidate position is offered for an initial period of 3 years (1st year 75%, 2nd and 3rd year 100%) with a possibility of extension. We are looking for highly motivated researchers with excellent communication skills who want to join our group. Further information on our research and our international research team can be found at

Your profile

    The successful candidates should have:

    • Master or Diploma university degree in either Mechanical Engineering, Computer Science, Applied Mathematics, or Physics

    Following competences are desirable:

    • The applicants should have an excellent scientific background in one or more of the following research disciplines: fluid mechanics, in particular turbulence, scientific computing including parallel programming, machine learning and/or quantum computing.
    • Experiences in scientific programming (such as Fortran90, Python, MPI, OpenMP, or OpenACC) and/or quantum computing are welcomed.

      Please provide a cover letter describing your personal qualifications and motivation, a CV, a list of publications if applicable, an evidence of the qualifications required for the position by corresponding certificates, and 1 references with contact information.

        What we offer you:

        From individual advancement with a wide range of continuing education opportunities to a variety of health and recreational offerings, you can expect an appreciative working environment at a renowned university at TU-Ilmenau. In addition to an attractive, familyfriendly working hours model, you will also benefit from advantages such as the use of the dining halls of the Studierendenwerk Thüringen, as well as participation in the various attractive sports offerings of the University Sports Center.

        The university stands in the fields of technology, mathematics and natural sciences, business and media for teaching and research at the highest level. She attaches particular importance to innovative teaching and interdisciplinarity. She identifies with Humboldt's ideals and pursues the vision of a cosmopolitan campus family.

        Technische Universität Ilmenau is holder of the „TOTAL E-Quality“ distinction and emphasizes gender equality.

        Severely disabled applicants with essentially identical professional suitability will be preferentially selected.

        Technische Universität Ilmenau offers flexible working time models.


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