24/11/2020
Logo of Marie Skłodowska-Curie Actions

PhD position in Physics-based machine learning for real-time simulation update

This job offer has expired


  • ORGANISATION/COMPANY
    RWTH Aachen University
  • RESEARCH FIELD
    GeosciencesOther
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    09/12/2020 00:00 - Europe/Brussels
  • LOCATION
    Germany › Aachen
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    39,83
  • OFFER STARTING DATE
    01/02/2021
  • EU RESEARCH FRAMEWORK PROGRAMME
    H2020 / Marie Skłodowska-Curie Actions
  • MARIE CURIE GRANT AGREEMENT NUMBER
    956965
  • IS THE JOB RELATED TO STAFF POSITION WITHIN A RESEARCH INFRASTRUCTURE?
    Yes

OFFER DESCRIPTION

Given the challenges in geothermal operations and the ambitious expansion plans for geothermal energy in many countries, there is an urgent need for experts with a broad understanding of geothermal systems. How can such systems be operated in the most efficient and safe manner? Within the Marie Curie Innovative Training Network EASYGO - Efficiency and Safety in Geothermal Operations, funded by the European Commission, the IDEA League universities TU Delft (TUD), ETH Zurich (ETH), RWTH Aachen University (RWTH), Politecnico di Milano (PoliMi) and 10 industry partners are addressing this question from different perspectives, integrating geology, geophysics, geochemistry, advanced modeling, and process engineering. In this framework, EASYGO is looking forward to train tomorrow’s leading geothermal energy experts. Please find additional information on EASYGO as well as detailed project descriptions of all 13 PhD positions on www.idealeague.org/geothermal-energy. In case you want to apply for multiple EASYGO positions, limit the number of applications to 3, and indicate other positions you have applied for together with a clear order of preference in your motivation letter.

Geothermal operations require reliable predictions of temperature and pressure fields to ensure sustainable and safe long-term operation. In this project, you will develop a numerical method to enable a real-time update of hydrothermal simulations. The aim is to react quickly to new measurements and to adjust results accordingly, in order to provide the best possible up-to-date prediction of the system state for subsequent decisions. The developments are based on existing open-source hydrothermal simulations in an efficient High-Performance Computing Finite Element Framework (MOOSE). This code will be adjusted to the geothermal simulation requirements and linked to a physics-based machine learning approach. Different geological parameterization approaches can then be tested and implemented in the workflow, with the aim to train machine learning systems for a real-time update of the hydrothermal state of a geothermal system. The work contains innovative and novel aspects from applied mathematics and computer science, with strong links to geophysical and geological data. The project is therefore interdisciplinary in nature and will have strong links to other projects within the ITN EASYGO.

Work is mainly conducted at the research unit Computational Geoscience and Reservoir Engineering (CGRE) at RWTH Aachen University, with planned secondments at industry partners and ETH Zurich. The CGRE team consists of 10 people of various scientific background, including geology, geophysics, and engineering. You can expect a very diverse and challenging job in an international and multidisciplinary working environment. RWTH Aachen University is part of the ABC/J Geoverbund, and therefore well connected with Cologne and Bonn Universities and the Forschungszentrum Jülich. RWTH is an equal opportunity employer. Thus, we specifically encourage applications of qualified women and minorities.

How to apply

Please submit your application no later than December 9, 2020 as a single PDF file named EASYGO_10_YourLastname_YourFirstname.pdf containing a motivation letter, CV, M.Sc. certificate and grades for the individual courses (highlighting courses relevant for this position) to applications@cgre.rwth-aachen.de. Language certificates and documents showing practical experience in the field are desirable. Please note that this file will be shared with members of the EASYGO recruitment committee at all four participating IDEA League universities and one industry representative.

Please note that EU ITN mobility rule applies (see guide for applicants here: https://ec.europa.eu/research/participants/data/ref/h2020/other/guides_f...), meaning that applicants must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting beneficiary for more than 12 months in the 3 years immediately before the recruitment date.

Your tasks

  • Adjust existing hydrothermal simulation codes (in MOOSE framework. C++) to geothermal problems
  • Train different surrogate models with focus on physics-based approaches (e.g. reduced basis method)
  • Integrate geological parameterizations, e.g. geological layers and spatial property distributions
  • Enable real-time simulation adjustments during operational and monitoring phase
  • Active participation in the various training and networking opportunities provided by EASYGO and the IDEA League doctoral school
  • Presentation of own research at selected scientific conferences and publication in peer-reviewed journals

More Information

Offer Requirements

Skills/Qualifications

  • University degree (Master or equivalent) in geosciences, engineering, computer science or related subjects
  • Experience with numerical simulations (preferably with Finite Elements) in theory and practice
  • Interest in coupled process simulations in geosciences
  • Experience with High Performance Computing is an advantage
  • Good English knowledge (written & spoken)
  • High degree of initiative, discipline and team spirit
  • Ability to work scientifically and independently

Work location(s)
1 position(s) available at
RWTH Aachen University
Germany
Aachen
52056
Wüllnerstr. 2

EURAXESS offer ID: 580239

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