Ph.D. Position in Data-driven Design of Multi-functional Materials (m/w/d)

 

A Ph.D. position is available on the development of novel computational methods and tools that will accelerate the design of multi-functional materials. The project involves making use of simulation or experimental data in order to build efficient, predictive models of materials’ properties. Due to the expense and time-demans of experiments and simulations, limited data would be available and the methods developed should be able to operate in the Small Data regime and quantify predictive uncertainty. More importantly the resulting models should be incorporated in a (stochastic)optimization framework that can efficiently search over the material design space and identify configurations leading to tailored or improved properties. The project is part of the multi-disciplinary team and involves close collaborations with other groups from Computer Science, Chemistry and Engineering.

The research topic combines physical modeling of materials, computational mathematics and (probabilistic)machine learning. Prior experience in computational optimization and statistical learning are highly desirable.

The work undertaken requires creative and analytical thinking and proficiency in scientific computing, often beyond the boundaries of traditional engineering. We aim at educating the best minds in a range of scientific fields and developing their ability to produce new knowledge that advances scientific understanding and encompasses a wide range of applications. As a result, the completion of a PhD in our group can provide many academic and industrial opportunities for employment.

The Professorship of Continuum Mechanics is part of Department of Engineering Physics and Computation in the School of Engineering and Design at the Technical University of Munich. The research efforts of the group center around the computational modeling of stochastic aspects with particular emphasis in solids/materials. Topics investigated include uncertainty propagation in physical and engineered systems, design and optimization in the presence of uncertainty, Bayesian inverse problems with applications in biomechanics, coarse-graining of atomistic descriptions and modelorder reduction.

What to expect:

• Students in our group follow a study and research plan that is adapted to their individual backgrounds, skills and goals.
• Students are integrated with our research activities immediately. In addition to taking graduate level courses, students are expected to devote time for studying relevant scientific publications and code writing.
• There will be daily, informal interaction with Prof. Koutsourelakis and other members of the group.
• Students will be encouraged and supported to interact with researchers in other groups within TUM and other universities and will get the opportunity to present their work in international meetings and conferences.
• Teaching duties will be in accordance with TUM regulations.

Qualifications:

• We are interested in self-motivated, focused and hard-working individuals with an ability for creative and innovative thinking.
• We are interested in applicants with strong academic backgrounds from all Engineering fields as well as Computer Science, Applied Mathematics, Applied Physics.
• Candidates should have already completed their M.Sc. degree or planning to complete it in the very near future.

How to Apply:

Interested candidates should apply by emailing Prof. P.S. Koutsourelakis by May 21th 2021 at the following address:

contmech@mw.tum.de

with the Subject: Ph.d. position Data-driven Design of Multi-functional Materials and include (in PDF format):

• a CV.
• an (unofficial) transcript of your academic record including courses/grades.
• a brief statement of your research experience. The latter should include a short description of your research interests and skills and how these integrate with the topics of Bayesian/probabilistic modeling, data-driven methods and optimization schemes.

You are encouraged to contact Prof. Koutsourelakis (p.s.koutsourelakis@tum.de) if you have any questions on the requirements or the research activities of the group. Evaluation of applications will start immediately. The initial contract offered will have a duration of one year and upon satisfactory progress, it can be extended until the completion of the Ph.D. The salary is in accordance with the German public service salary scale (100% TV-L E13). TUM is an equal opportunity employer. TUM aims to increase the proportion of women and therefore particularly welcomes applications by women. Applicants with severe disabilities will be given priority consideration given comparable qualifications.

As part of your application for a position at the Technical University of Munich (TUM), you submit personal data. Please note our privacy policy in accordance with Art. 13 General Data Protection Regulation (DSGVO) http://go.tum.de/554159 for the collection and processing of personal data in the context of your application. By submitting your application, you confirm that you have read the privacy notice of TUM.