PhD Candidate on EAISI – DAMOCLES: Robust Bayesian Uncertainty Quantification

Updated: about 1 month ago
Deadline: 30 Sep 2021

The Eindhoven University of Technology, Department of Electrical Engineering has a PhD vacancy on EAISI DAMOCLES: Robust Bayesian Uncertainty Quantification in the Department of Mechanical Engineering in collaboration with Mathematics and Computer Science.


Mechanical Engineering, Electrical Engineering, Mathematics and Computer Science

Institutes and others

EAISI - Eindhoven Artificial Intelligence Systems Institute

Reference number


Job description

The modeling of complex engineering systems is highly challenging. Physics-based models require a cautious application of constitutive assumptions, whereas data-based models require vast amounts of data. The research project “DAMOCLES: Data-Augmented Modeling Of Constitutive Laws for Engineering Systems”, of which this PhD position is a part, targets a breakthrough in the constitutive modeling of such systems in different physical domains by developing a unified multi-tool framework that combines the favorable characteristics of physics-based and data-based approaches. DAMOCLES is an inter-departmental project part of the Eindhoven Artificial Intelligence Institute (EAISI) Exploratory Multidisciplinary AI Research Program (EMDAIR).

The DAMOCLES project is divided into three interlinked sub-projects, namely:

  • Black-Box Modeling with Physics-Based Constraints
  • Robust Bayesian Uncertainty Quantification
  • Data-driven constitutive modeling
  • The second sub-project is advertised here (the others can be accessed through the TU/e vacancy bank). This open PhD position aims to develop a robust uncertainty quantification (UQ) framework for evidence-based constitutive model selection, aiming to find the model with the best trade-off between model bias and inference imprecision. This will be realized by merging state-of-the-art Bayesian inference techniques with robust uncertainty quantification models.

    Bayesian uncertainty modeling provides an elegant approach to select and calibrate physical models. It systematically combines physical laws, prior information, and data. Bayesian inference for constitutive modeling is challenging, however, since in practice models are biased, prior information is incomplete, and data is scant. During this project, the PhD candidate will develop a Bayesian uncertainty quantification framework to select and calibrate constitutive models. To gain fundamental understanding, initially a relatively simple hierarchical set of constitutive models will be considered. Once the UQ framework is well understood, the framework will be extended to be applicable to a complex set of constitutive models. The PhD candidate will develop robust UQ models and sampling-based Bayesian inference techniques (e.g., Markov-Chain Monte-Carlo) to enable optimal model selection from this complex set of constitutive models and optimal calibration of the selected model.

    To accomplish the objectives of the DAMOCLES project, a strong cooperation with the PhD candidates and researchers in the other sub-projects is required. In particular, the results obtained in this PhD project will be evaluated and analyzed on a selected set of overarching benchmark applications.


    • Study the literature of uncertainty quantification, Bayesian inference, robust uncertainty modeling, Markov-Chain Monte-Carlo techniques and constitutive modeling.
    • Develop detailed insight into the limitations of state-of-the-art uncertainty approaches in relation to the (constitutive) model complexity and data-availability.
    • Develop a sampling-based technique for model selection and calibration suitable for complex constitutive relations.
    • Develop an algorithm that makes it computationally tractable to consider robust uncertainty models.
    • Dissemination of the results of your research in international and peer-reviewed journals and conferences.
    • Writing a dissertation based on the developed research and defending it.
    • Assume educational tasks like the supervision of student projects.
    • Integration in the Eindhoven Artificial Intelligence for Systems Institute.

    Job requirements

    We are looking for a candidate who meets the following requirements:

    • You are a talented and enthusiastic young researcher.
    • You have experience with or a strong background in (computational) modeling, mathematics and statistics. Preferably you finished a master’s in Mechanical Engineering, (Applied) Physics, (Applied) Mathematics, Information Technologies, or similar.
    • You have good programming skills and experience (Python is an asset).
    • You have good communicative skills, and the attitude to partake successfully in the work of a research team.
    • You are creative and ambitious, hard-working, and persistent.
    • You have good command of the English language (knowledge of Dutch is not required).

    Conditions of employment
    • We offer a challenging job  in a highly motivated team at a dynamic and ambitious University. You will be part of a highly profiled multidisciplinary collaboration where expertise of a variety of disciplines comes together. The TU/e is located in one of the smartest regions of the world and part of the European technology hotspot ‘Brainport Eindhoven’; well-known because of many high-tech industries and start-ups. A place to be for talented scientists!
    • A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
    • To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
    • To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program ).
    • A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
    • Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
    • Should you come from abroad and comply with certain conditions, you can make use of the so-called ‘30% facility’, which permits you not to pay tax on 30% of your salary.
    • A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
    • Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.

    Information and application

    Do you recognize yourself in this profile and would you like to know more?
    More information about the project can be obtained through the project’s supervisory team:
    Dr. C.V. Verhoosel and Dr. E. Quaeghebeur .

    For information about terms of employment, click here or contact H. Boulgalag, HR Advisor, email: h.boulgalag[at]

    Please visit to find out more about working at TU/e!


    We invite you to submit a complete application by using the 'apply now'-button on this page.
    The application should include a:

    • a cover letter (stating personal goal and research interests connecting to one or more of the topics defined above),
    • a complete Curriculum Vitae (including a list of publications, if any),
    • transcripts of BSc and MSc degrees,
    • at least one recommendation letter.
    • at least one recommendation letter.

    All documents should be provided in pdf format. We do not respond to applications that are sent to us in a different way.

    Both national and international applications to this advertisement are appreciated. Review of applications will begin immediately and continue until the position is filled. Promising candidates will be contacted by email.

    We do not respond to applications that are sent to us in a different way.

    Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary, please combine files.

    We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.

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