Teaching assistant Jobs in Hoppegarten

About us

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.

Founded in 2003, the University of Luxembourg is a research university focused on tomorrow's major societal challenges, including digital transformation, medicine and health, and sustainable and societal development. With over 1,000 doctoral candidates among 7,500 students as well as 300 professors, the University distinguishes itself with its international and multilingual character, an interdisciplinary approach, and a personalised learning environment.

The Department of Mathematics (DMATH) at Luxembourg University is recognized internationally for leading research in both fundamental and applied areas of Mathematics.

DMATH offers a Bachelor's program and three Master's programs, alongside a robust range of outreach activities, and is dedicated to training the next generation of researchers - PhD candidates and postdoctoral scholars. With a permanent staff comprising 12 full professors, 2 associate professors, and 1 assistant professor, DMATH covers a wide spectrum of research areas : (1) Algebra and Number Theory, (2) Geometry and Topology, (3) Probability and Stochastic Analysis, (4) Discrete and Geometric Analysis, (5) Statistics and Data Science, and (6) Partial Differential Equations and Modelling.

Your role

The Department of Mathematics (DMATH) is seeking to fill a new Assistant Professor position in mathematics, in the area of Statistical Learning.

The selected candidate will be expected to establish an internationally recognized research group in Statistical Learning, to contribute to the teaching mission of DMATH at every level of mathematical education, as well as to engage in outreach activities.

The successful candidate will also have a significant teaching experience and will contribute to the teaching of statistics and mathematics within and outside DMATH.

Contact : Prof. Dr. Giovanni Peccati, Head of DMATH :

Your profile

The successful candidate will have made important contributions to this area and published in top ranked peer reviewed international journals and / or conference proceedings. Topics of interest within the expertise of the candidate include (but are not limited to) :

• Uncertainty quantification
• Regularization techniques
• Machine learning
• deep learning
• High‐dimensional statistics
• Dimensionality reduction
• Causal machine learning
• Reinforcement learning
• Gradient methods

Experience in outreach and science communication is considered an important selection criterion.

The candidate will have demonstrated her or his ability to carry out administrative duties. S / he is expected to apply for joint and individual third‐party funding (such as calls of the Luxembourg FNR, or of the European ERC program). Evidence of this ability (as portrayed by the candidate's track record) will be a crucial selection criterion.

Language requirements :

The University of Luxembourg is set in a multilingual context. The person hired on this position must be proficient in English and either French or German. The University encourages its staff to learn the other language and provides access to language courses to this end.

We offer

How to apply

Submit application online including :

Full list of publications

All applications will be handled in the strictest confidence. Applications by post or e-mail cannot be considered. We encourage early application. To ensure full consideration, candidates should apply by June 30

2025.

General Information :

Gender equality :

In line with our values, the University of Luxembourg encourages an inclusive culture. We promote equality of opportunity, diversity and a working and learning environment in which the rights and dignity of everybody is respected. The University is committed to achieving gender parity among its academic staff. Should candidates present equivalent CVs, preference will be given to female candidates in all departments where gender parity is not yet achieved.