Student Research Assistant

UniDistance Suisse; Oberwallis; Hybrid; CHF 17.000 - 23.000; Teilzeit

Zusammenfassung

UniDistance Suisse in Brig is seeking a Student Research Assistant (25%) to contribute to numerical methods for designing acoustic metamaterials. The role involves implementing and testing methods in Python and Matlab, and collaborating with PhD students on a highly applied mathematics project.

The position offers opportunities to write an MSc thesis within the project scope, with a start date on or after 1st October 2026. Place of work is Brig; remote work is possible.

Qualifikationen

• Advanced student with very good grades in applied mathematics, scientific computing or engineering.
• Strong programming skills in Python or Matlab.
• Very good English skills.

Aufgaben

• Support efficient implementation and testing of methods for acoustic metamaterial design in Python and Matlab.
• Collaborate closely with PhD students in the project.
• Option to write a Master thesis within the project scope.

Kenntnisse

Python

Matlab

English

Ausbildung

Advanced student in mathematics/scientific computing/engineering (Swiss higher ed)

Jobbeschreibung

The recently approved SNF project, entitled “Numerical Methods for Efficient Acoustic Metamaterial Design” aims to design acoustic metamaterials which are used to manipulate sound waves, with the objective of, for instance, mitigating sound within the audible frequency spectrum. For the optimal design of such metamaterials our goal is to optimise models from (mixed) boundary/finite element discretisations. This leads to very rich structures in the underlying linear systems of equations. The aim of the project consists of fully exploiting these structures for efficient matrix storage as well as in the numerical solution techniques. These techniques will open up the possibility to apply parametric model reduction methods which are the basis for determining an optimal acoustic metamaterial. The project will be carried with our project partner Prof. Dr Steffen Marburg (Chair of Vibroacoustics of Vehicles and Machines, Technical University of Munich, Germany).

We are looking for a:

Student Research Assistant (25%)

The recently approved SNF project, entitled “Numerical Methods for Efficient Acoustic Metamaterial Design” aims to design acoustic metamaterials which are used to manipulate sound waves, with the objective of, for instance, mitigating sound within the audible frequency spectrum. For the optimal design of such metamaterials our goal is to optimise models from (mixed) boundary/finite element discretisations. This leads to very rich structures in the underlying linear systems of equations. The aim of the project consists of fully exploiting these structures for efficient matrix storage as well as in the numerical solution techniques. These techniques will open up the possibility to apply parametric model reduction methods which are the basis for determining an optimal acoustic metamaterial. The project will be carried with our project partner Prof. Dr Steffen Marburg (Chair of Vibroacoustics of Vehicles and Machines, Technical University of Munich, Germany).

We are looking for a:

Student Research Assistant (25%)

Place of work: Brig. Remote work is possible.

Start date: 1st October 2026 or later upon agreement.

Your responsibilities

• Support of the efficient implementation and testing of the methods for acoustic metamaterial design, e.g., in Python and/or Matlab.
• Close collaboration with the Ph.D. students who are working in the project.
• An M.Sc. thesis can be written within the scope of the project (optional).

Your profile

• Advanced student at a Swiss higher education institution in (applied) mathematics, scientific computing or engineering with very good grades.
• Strong programming skills, e.g., in Python or Matlab.
• Strong interest in working on a very applied mathematics project.
• Background in applied mathematics with a focus in one or more of the following subfields is a plus: numerical linear algebra (e.g., rank-structured matrices, tensor techniques), model reduction, numerical methods for partial differential equations, machine learning, numerical optimisation.
• Independent working style, stamina and curiosity.
• Very good English skills.

We offer

• A research-intensive academic environment: You will be part of a dynamic university setting characterised by strong scientific expertise and a culture of interdisciplinary collaboration.
• Attractive employment conditions: modern working conditions, generous social benefits and a wide range of continuing education opportunities.
• A committed team: you will join a highly skilled and engaged team that embodies innovation, excellence and a strong culture of collaboration.
• An inspiring working environment: our campus in Brig is located in immediate proximity to the historic old town, the Swiss Federal Railways (SBB) station and attractive natural and mountain regions. It offers a high quality of life and a vibrant academic exchange.