Ph.D./Postdoc position in data-driven modeling of engineering materials

Contract type: Temporary
Job status: Full-time
Hours per week: 40
Application deadline: 31.08.2022
Job start date: 01.10.2022
Department: Department of Mechanics


We are looking for a Ph.D. candidate or a postdoctoral researcher who will conduct research in the areas of Optimization, Semantics, Data-driven modeling, and Machine Learning. The position will be embedded in the research group working on the ongoing project: “SUMO: Sustainable design empowered by materials modeling, semantic interoperability and multi-criteria optimization”, focusing on application-driven research in computational data-driven methods for material design.

The research topic for this position will be within the areas of optimization and multiscale modeling, both in probabilistic settings and in combining data and expert knowledge of underlying physical principles. The proposed research aims at combining and integrating:

(a) physical-based and data-driven modeling and simulation,

(b) material microstructure generation, reconstruction, and analysis,

(c) Bayesian statistics, and

(d) sensitivity and inverse analysis.

Sparse polynomial chaos, which serves as the digital twin, will be trained with both the real data and the synthetic data obtained from a stochastic multiscale computational model. This strategy enables the use of an interpretable model (physics-based) to build a fast material digital twin (machine learning) that will be connected to the physical twin to support material design decisions.

The developed methods and tools will be integrated into an existing open, interoperable simulation platform, MuPIF. The developments will be used to support design problems such as sustainable best-fitting materials, microstructural design, predicting microstructures morphology, extracting properties based on materials microstructure, and material structures performance evaluations.

The position will be supervised by Dr. Anna Kučerová (probabilistic inverse methods and optimization, Fulbright and L’Óreal-UNESCO Awardee, currently on sabbatical at MIT with the Uncertainty Quantification Group).

Eligibility criteria

For a Ph.D. position, we are looking for applicants with a Master of Science degree or equivalent. Therefore, students with an M.Sc. in Materials Science, Data Science, Applied Mathematics, or related fields are invited to apply. Previous expertise in finite element-based modeling, material modeling, machine learning, and methods for uncertainty quantification or optimization will be valued. Working knowledge of programming and the English language is required.

For a postdoctoral position, we additionally expect a close match between research conducted so far and the scope of the project.

Interested candidates are invited to provide their basic contact information at:

The application package should contain:

●     Motivation letter (up to two pages), stating personal goals and research interests.

●     Academic curriculum vitae including a detailed list of publications, when applicable.

●     Contact details for two senior researchers who could support the application.

●     For a Ph.D. position.

○     A transcript of your Master's (required) and Bachelor's (recommended) grades.

○     A copy or a link to your M.Sc. thesis.

○     Date of your M.Sc. degree award or the expected date of M.Sc. thesis defense.

●     For a postdoctoral position.

○     A copy or a link to your Ph.D. thesis.

○     Date of your Ph.D. award or the expected date of your Ph.D. thesis defense.

The file can be (preferably) submitted via the application form specified above or by email to: Tato e-mailová adresa je chráněna před spamboty. Pro její zobrazení musíte mít povolen Javascript.

For full consideration, complete applications must be received by 31 August 2022. We reserve the right to disregard incomplete applications.

Review of applications will begin immediately and continue no later than 15 September 2022, when the evaluation committee will invite shortlisted candidates to the interview stage (in-person for applicants living in the Czech Republic; remote for all other candidates).

We offer:

●     Participation in an ambitious interdisciplinary research project exploring the interfaces between machine learning and computational mechanics.

●     An initial appointment for 2 years (with an extension to 4 years, in the case of mutual interest, for the Ph.D. position).

●     Net salary of about 54K CZK monthly for a postdoc position and about 33K CZK monthly for a Ph.D. position with the possibility of applying for additional student stipends (min. 9K CZK net monthly); see the Numbeo database for the cost of living in Prague.

●     An informal and inclusive international working environment at the Department of Mechanics, Faculty of Civil Engineering.

●     Close collaboration with the Luxembourg Institute of Science and Technology, the partner institution for the SUMO project.

●     Office facilities located on the Czech Technical University in Prague’s Dejvice campus. Prague regularly ranks among the top five European cities to live in (cf. Time Out Magazine index for 2021).

●     Full social and health insurance.

●     30 days of paid annual leave.

●     Children’s corner, kindergarten, and elementary school operated by the Czech Technical University in Prague.

●     Additional benefits such as subsidized meals, yearly benefits supporting recreational and sports activities, as well as health care programs.

●     Relocation assistance via the EURAXESS Czech Republic office.

For more information:

●     About the project and research topics: please feel free to contact Anna Kučerová at: Tato e-mailová adresa je chráněna před spamboty. Pro její zobrazení musíte mít povolen Javascript..

●     About employment conditions or living in Prague: please consult the comprehensive info collected by EURAXESS Czech Republic or contact Eliška Blümlová at: Tato e-mailová adresa je chráněna před spamboty. Pro její zobrazení musíte mít povolen Javascript..

●     About Ph.D. studies at CTU, please consult the basic information compiled here.