Postulez pour le programme de recherche en science des données au MUDS en Allemagne !


Deadline : 5 juillet 2023

Voici tous les détails en anglais.

The Munich School for Data Science (MUDS) in Germany, Munich, is currently accepting applications for several PhD positions in data science. This research program offers an exceptional opportunity for both domestic and international students with backgrounds in computer science, data science, biomedicine, plasma physics, earth observation, and robotics. MUDS aims to provide specialized training in data science and scientific domains through collaborations with leading institutions and centers. If you are interested in pursuing a PhD in data science, continue reading to discover more about this remarkable opportunity.

MUDS focuses on training doctoral candidates in various aspects of data science and its applications in scientific disciplines pursued at the three participating Helmholtz centers. The program encompasses a wide range of topics and projects, including large-scale data management, data mining, data analytics, machine learning, deep learning, high-performance computing, and high-performance analytics. Additionally, research areas such as data integration, uncertainty quantification, model-order reduction, and multi-fidelity methods are also explored.

Munich School for Data Science (MUDS) is a collaborative initiative between the three Helmholtz centers in the Munich area, namely Helmholtz Zentrum München, Max Planck Institute of Plasma Physics, and the German Aerospace Center. It also involves partnerships with Ludwig-Maximilians-Universität München (LMU), the Technical University of Munich (TUM), Leibniz Supercomputing Center (LRZ), and the Max Planck Computing and Data Facility (MPCDF).

As a successful applicant, you will enjoy several benefits. First, there are no tuition fees for any of the programs offered at MUDS. Second, PhD students receive financial support throughout their studies. Moreover, if you are invited for an on-site interview, your travel expenses will be reimbursed, ensuring that financial constraints do not hinder your participation.

Eligibility criteria 

To be eligible for the PhD positions at MUDS, candidates must meet the following criteria:

  • Applicants of all nationalities are welcome to apply.
  • A university master's degree (MSc or equivalent) in a relevant discipline is required.
  • For enrollment at either TUM or LMU, a master's thesis must have been completed during your studies.
  • If you have not yet completed your master's degree by the application deadline, you can provide a provisional certificate or a bona fide statement from your university indicating the marks obtained thus far and an estimated graduation date.

Successful completion of the master's degree is a requirement for acceptance into the program.

How to apply ?

To apply, interested candidates must submit their applications online through the program application portal. The following documents are required:

  • Master's diploma, certificate, and transcript of records in German or English.
  • Bachelor's diploma, certificate, and transcript of records in German or English.
  • If your certificates were not issued in English, include an original and official English translation.
  • High school certificate.
  • Curriculum Vitae (CV) in PDF format.
  • Two letters of recommendation to be submitted directly by the referees.
  • Proof of English language proficiency for non-native speakers.

The Munich School for Data Science (MUDS) presents an incredible opportunity for aspiring researchers and data enthusiasts to pursue a PhD in data science in Germany. The program offers a comprehensive curriculum and access to state-of-the-art facilities, fostering an environment for cutting-edge research in various scientific domains. If you have a passion for data science and wish to contribute to advancements in this field, don't miss this chance to apply for the MUDS PhD positions. Share this opportunity with your friends and colleagues who may be interested and leave your thoughts and questions in the comments section below. Let's embrace the power of data and shape the future of science together!

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