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M-22105 PhD STUDENT DATA-DRIVEN APPROACHES IN SOLID MECHANICS

M-22105 PhD STUDENT DATA-DRIVEN APPROACHES IN SOLID MECHANICS

Are you passionate about research? So are we! Come and join us

The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies. 

https://www.list.lu/


You ‘d like to contribute as a PhD student? Join our Materials Research and Technology department 

Through its research into advanced materials and processes, the “Materials Research and Technology” (MRT) department, with its 200 researchers and engineers, contributes to the emergence of enabling technologies that underpin the innovation processes of local and international industry. MRT’s activities hinge on four thematic pillars: nanomaterials and nanotechnology, scientific instrumentation and process technology, structural composites, and functional polymers. The department also includes four high-tech platforms, focusing on composites, prototyping, characterization and testing. These platforms serve both LIST research staff, and other stakeholders in Luxembourg.

Responsibilities

How will you contribute?

We are looking for a PhD student who will do research in the area of Optimization, Semantic, data driven and Machine Learning. The position is embedded in the Composite Modelling Research group. The group focuses on application-driven research in computational data driven methods for material materials design by sustainability. The research project for the advertised position will be within the areas of optimization and multiscale modelling, both from data and underlying physical principles. The proposed research aims at combining and integrating (a) physical-based and data-driven modelling and simulation, (b) material microstructure generation, reconstruction and analysis, and material informatics for sustainable composite modelling. A Bayesian machine learning classifier, that serves as the digital twin, will be trained with data taken from a real data and stochastic multiscale computational model. This strategy allows 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 in an existing open knowledge-based and interoperable computational platform. The developments will be used to support the design problems such sustainable best-fitting materials, microstructural design, predicting microstructures morphology, extracting properties based on materials microstructure and material structures performance evaluations.


As part of the team, you will particularly focus on the group’s activities around knowledge-based decision support tools and software platforms for material modelling and design. You will have the opportunity to learn, develop and apply a range of cutting-edge modelling computational and experimental techniques, including computational mechanics tools, machine learning tools, digital image and digital volume correlation, and X-ray microtomography.

must have requirements

Is Your profile described below? Are you our future colleague? Apply now!

  • Hold a master degree in applied mathematics or computational engineering or related topics
  • Have an excellent knowledge of continuum mechanics and finite element analysis
  • Have a proven experience with machine learning 
  • Are keen on bridging machine learning tools with solid mechanics knowledge
  • Have good knowledge in programming and application design and experience in application development using Python, Java/JEE and related technologies.
  • You must be able to communicate in English. Knowledge of French, German or Luxembourgish is an asset.
We offer

Your LIST benefits

An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects; 

Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society;

Innovative infrastructures and exceptional labs occupying more than 5,000 square metres, including innovations such as our Viswall, high-scale incubators and top of the range 3D/4D printings that are part of our toolkit for excelling in all we do;

Multicultural and international work environment with more than 45 nationalities represented in our workforce;  

Diverse and inclusive work environment empowering our people to fulfil their personal and professional ambitions;

Gender-friendly environment with multiple actions to attract, develop and retain women in science; 

32 days paid annual leave, 11 public holidays, flexible working hours, 13-month salary, statutory health insurance and access to lunch vouchers; 

Personalized learning programme to foster our staff’s soft and technical skills; 

An environment encouraging curiosity, innovation and entrepreneurship in all areas. 


Apply online

https://www.list.lu/en/jobs/

Your application must include:

  • A motivation letter oriented towards the position and detailing your experience;
  • A scientific CV with contact details;
  • List of publications (and patents, if applicable);
  • Contact details of 2 references


Application procedure and conditions:

  • LIST is an equal opportunity employer and is committed to hiring and retaining diverse personnel. We value all applicants and will consider all competent candidates for employment without regard to national origin, race, colour, gender, sexual orientation, gender identity, marital status, religion, age or disability; 
  • Applications will be reviewed on an ongoing basis until the position is filled; 
  • An assessment committee will review the applications and select candidates based on guidelines that aim to ensure equal opportunities; 
  • The main criteria for selection will be the correspondence of the existing skills and expertise of the applicant with the requirements mentioned above.


PhD additional conditions:

  • Supervisor at LIST: Dr. Salim Belouettar (salim.belouettar@list.lu)
  • Co-supervision at LIST: Gaston Rauchs 
  • Work location: Luxembourg Institute of Science and Technology (LIST), Hautcharage, Luxembourg
  • PhD enrolment: University of Prague
  • Candidates shall be available for starting their position in spring 2022, between March and May 2022. Please note the universities costs are at the charge of the student.

REQUIREDLANGUAGES

To be considered for this position it is crucial that you have knowledge of the following languages
  • Read C1 Advanced
    Write C1 Advanced
    Speak C1 Advanced
minimum required Education
Required work experience in years
0 or more years
Details
Employment type
Contract type
Hours per week
40
Contract period
Months
Contract duration
48
Location
Country
City
Bascharage
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