PhD student position in Machine learning accelerated segmentation

Are you curious to develop an extensive competence in advanced computational modelling of novel composite materials, accelerated by machine learning tools? Then please apply for this PhD student project where we aim to establish an efficient numerical framework to automatically go from a number of physical material samples to the statistical prediction of mechanical properties of 3D-reinforced composites.

Project description
With this project, you will have the opportunity to work on challenging but inspiring tasks. In the first phase, we will develop a machine learning-based method for segmentation of Computer Tomography images into geometrical models of the material architecture in 3D reinforced composite materials. With this at hand, the next phase will target to develop a method for automated transformation of the geometrical information of the material into numerical finite element models of the same. Finally, these models will be analysed to predict the material response under multiaxial loading of 3D-refinforced composite materials, and how these are influenced by statistical variations in the material architecture.

Your PhD student project will be part of the Marie Skłodowska-Curie Action Doctoral Network (MSCA DN) “Real-time characterization of anisotropic carbon-based technological fibres, films and composites” (RELIANCE). The multidisciplinary network spans a wide area of competence -- from expertise in imaging and machine learning tools to excellence in material mechanics -- and is concerned with R&D and training in X-ray imaging and scattering tools and its applications in materials and information science.

The network offers in total 14 PhD positions. You can find information on all 14 PhD positions here (14 PhD positions in real-time characterization for materials engineering | EURAXESS (europa.eu)). Via the extensive network training provided by RELIANCE, you will participate in summer schools, workshops, and scientific meetings together with top scientists and your 13 fellow PhD students in the RELIANCE network. You will also carry out one secondment at the Technical University of Denmark to learn the principles of X-ray Computer Tomography, and one at Volvo Car Group exploring the industrial use of the tools developed in the project. 

The position placed at the Division of Material and Computational Mechanics is meritorious for future research duties within academia as well as in industry and the public sector. As part of your engagement at the division you will be expected to teach (up to 20 % of the time). This experience will further prepare you for future academic jobs, but also for communication tasks in industry.

The work will be carried out in close collaboration with Prof. Martin Fagerström at the Division of Material and Computational Mechanics, Chalmers University of Technology and Prof. Lars Pilgaard Mikkelsen at the Department of Wind and Energy Systems, Technical University of Denmark. In addition, special expertise on imaging techniques and machine learning will be offered by other scientists in the network to the extent needed.

Major responsibilities
As a PhD student, you will be central to the project developments, and responsible for the numerical implementation and validation of all tools developed in the project. You are expected to develop your own scientific concepts and communicate the results of your research verbally and in writing. Your research activities will contribute to enhanced knowledge in the scientific field, in particular by presenting your results in scientific journals and at international conferences.

Qualifications
We expect that you are a highly motivated and self-propelled person, with a distinct interest in the mechanics of composite materials and advanced numerical tools. You need to have a Master's degree in Solid and/or Structural Mechanics, Engineering Physics, Engineering Mathematics or similar, with documented high competence in computational methods and a strong foundational knowledge in the mechanics of composite materials. Furthermore, good capabilities in numerical analysis and programming are a must.

Since communication of knowledge results towards academic and industrial partners is a central part of the work, communicative skills in English (oral as well as in writing) are vital. Furthermore, it will also be expected that you can take on responsibilities within the project, have the ability to take own initiatives and, when needed, work independently. At the same time, being a successful researcher also involves working with others as well as disseminating the results within existing and new networks, both within academia but also to industry and society. Therefore, networking skills, teamwork skills and quality assurance are important.

Knowledge of the Swedish language is desirable but not a requirement. Chalmers offers Swedish courses.

Contract terms
Full-time temporary employment. The position is limited to a maximum of five years.

According to the requirements set by the Marie Skłodowska-Curie Action Doctoral Network programme, eligible candidates must be a doctoral candidate, i.e. not already in possession of a doctoral degree at the date of the recruitment. The candidate must also not have resided or carried out their main activity (work, studies, etc.) in Sweden for more than 12 months in the 36 months immediately before their recruitment date.

We offer
Chalmers offers a cultivating and inspiring working environment in the coastal city of Gothenburg. 
Read more about working at Chalmers and our benefits for employees.

Chalmers aims to actively improve our gender balance. We work broadly with equality projects, for example the GENIE Initiative on gender equality for excellence. Equality and diversity are substantial foundations in all activities at Chalmers.

More information and apply HERE

Application deadline: February 26, 2023

For questions, please contact:
Prof. Martin Fagerström, Material and Computational Mechanics
Email: martin.fagerstrom@chalmers.se
Tel: +46(0)31 772 13 00

*** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. ***