PhD studentship in Data-Driven Modelling and Control (DTNModConFlex)

PhD studentship in Data-Driven Modelling and Control (DTNModConFlex)

The Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship (FAU DCN-AvH) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) is looking for two PhD students to collaborate on the MoDConFlex, Marie Skłodowska-Curie Doctoral-Training-Network.

Where: FAU DCN-AvH, Department of Data Sciences. Cauerstr. 11, 91058 Erlangen, Germany
Vacancies: 02
Full-time
Salary: 3.342 EUR (gross) plus any applicable mobility and family allowances
Starting at: Earliest possible
Time limit: 3 years

We invite applications for a 36-month PhD studentship in the context of the Marie Skłodowska-Curie Doctoral-Training-Network (DTN) “ModConFlex”.
The network brings together 17 academic and industrial partners across Europe with the goal of developing novel approaches to the modelling and control of flexible structures. This project will create groundbreaking mathematical tools for data-driven modelling and control of partial differential equations, which will be efficient in practice and have rigorous theoretical foundations. It is envisioned that the project will combine ideas from a broad range of disciplines, including machine learning, control theory, PDE analysis, and randomized algorithms.

The successful applicants will be hired as a PhD candidate (Dienstvertrag contract) and will join an interdisciplinary team of applied mathematicians at the Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship in the Department of Data Science of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The project will be supervised by Prof. Enrique Zuazua and Prof. Giovanni Fantuzzi.

The successful applicants will also be fully integrated in the ModConFlex DTN. Collaboration with researchers at partner institutions and participation in training events organized by the DTN is expected.

Required Qualifications

Applicants must have:
• A Master Degree in Mathematics or related disciplines (Physics, Engineering, etc.). Applicants who do not yet hold such a degree must be awarded one before the project start date.
• A strong background in one or more of the following fields: Partial Differential Equations, Machine Learning, Data Science, Control Theory, Numerical Analysis.
• Experience with computer programming.
• Demonstrated capacity to conduct academic research at the highest international standards.
• Excellent knowledge of English (written and spoken)

In addition, applicants:
• Must not hold a PhD or be enrolled in another PhD program
• Must not have resided or carried out their main activity (work, studies, etc.) in Germany for more than 1 year out of the last 3 years. This is in line with standard mobility criteria for a standard Doctoral Training Network.
Knowledge of German is not required for this position. Please visit the website of the Department of German at FAU for information about language courses offered to students at FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg.

Opportunities

FAU expects applicants to become actively involved in administering academic affairs and in developing strategic initiatives. FAU offers career development, mentoring and an attractive initial research budget. Based on international standards and transparent performance agreements, FAU ensures a comprehensive and fair evaluation process.

In its pursuit of academic excellence, FAU is committed to equality of opportunity and to a proactive and inclusive approach, which supports and encourages all under-represented groups, promotes an inclusive culture and values diversity. FAU is a family-friendly employer and is also responsive to the needs of dual career couples.

How to Apply

Submit your application in a single pdf file via our talent platform (please, see below) with:
• Cover/motivation letter (max two A4 pages, 11pt Arial or Calibri font, min 2cm margin on all sides)
• Curriculum Vitae (CV)
• Master certificates or an official letter with the expected date of award
• List of publications (if any)
• List of additional potential referees
• Two (2) Reference letters, to be sent to by the applicant’s referees directly to: dcn-avh[@]fau.de
(One letter must be written by the applicant’s Master thesis supervisor. The letters should comment in particular on the applicant’s academic skills, suitability for the post, intellectual independence and potential to carry out excellent research.)

* Applications must be submitted via our online form and will be considered until the position is filled.

[IMPORTANT] Only applications submitted via this form will be considered!

Further information

For further information and informal enquiries about this position, please write an email to: giovanni.fantuzzi@fau.de

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