PhD Position Uncertainty Quantification of Physics-Based and Machine Learning Models of Critical Industrial Components

Updated: about 2 months ago
Deadline: 11 Feb 2024

The manufacturing sector is and will continue to experience an evolving trend, marked by the exponential growth of additive manufacturing, the ongoing 4th industrial revolution (Industry 4.0), and an increasing demand for the customization of manufactured products. However, the production and economic growth of the manufacturing sector need to meet unpostponable sustainability guidelines and criteria. This means minimizing energy losses as much as possible. To fully sustain this ongoing transition of the EU manufacturing sector, two key technical challenges need to be simultaneously tackled: the uncertainty induced by the production process (e.g., human errors, geometrical and material variabilities caused by the manufacturing process and/or by the machine setup) and the increasing complexity of the manufactured goods characterized by the critical parts or components.

In order to tackle these challenges, the Active PRoduct-to-Process LearnIng fOR Improving Critical Components Performance (APRIORI) training network has been created. This project (no. 101073551) is funded under the Marie-Sklodowska-Curie Actions Doctoral Networks within the Horizon Europe Programme of the European Commission.

The ambition of APRIORI is to develop new technologies that will enable, for the first time, the development of a unique integrated product design strategy that will drastically improve the performance of the manufacturing sector in Europe. APRIORI’s mission is to train the next generation of doctoral candidates to fully sustain the ongoing transition of the EU manufacturing sector. More precisely, APRIORI offers 15 doctoral positions within a multidisciplinary and international network, involving universities (KU Leuven, TU Delft, Aalborg University), a research institute (Jozef Stefan Institute), and small- and large-scale industry (Grundfos Holding AS, Materialise NV, Temporary Works Design 9 B.V., Qlector) from manufacturing, equipment design, manufacturing software, and data-driven solutions sectors with the relevant expertise to create the coordinated research environment needed to develop integrated design strategies to produce better, long-lasting, and sustainable products. In addition to their research activities, the doctoral candidates will participate in peer-educational initiatives, further enhancing the collaborative and scholarly dimensions of the program.

For this position, you will work on the development of efficient techniques for assessing uncertainties in the input parameters of physics-based models on the design performance of critical industrial components. You will investigate the combination different approaches for uncertainty quantification considering both physics-based models (Finite Element Models in particular, and its stochastic counterparts such as StatFEM) and machine learning-based surrogates (e.g. Multifidelity Gaussian Processes, Polynomial Chaos, Conformal Prediction). The key challenge will be to obtain efficient uncertainty metrics while avoiding large numbers of simulations of expensive-to-run high-fidelity models.

Close collaboration with other PhD projects within APRIORI is also envisioned, with special focus on comparing, combining and exploiting advanced probabilistic models developed by the other candidates (e.g. Bayesian networks, vine-copulas and novel Monte Carlo samplers). The project is embedded in a cross-departmental collaboration within the faculty of Civil Engineering and Geosciences, including the Department of Mechanics, Materials, Management and Design (3MD), the Department of Hydraulic Engineering (HE) and the Department of Engineering Structures (ES). You will be working on an exciting topic with a strong interdisciplinary nature.

We are in search of a highly motivated professional who enjoys working in a multidisciplinary setting.

If the following characteristics resonate with you, consider joining us on this PhD journey:

  • You already have a good background in numerical methods for scientific computing, with focus on computational mechanics tools such as the Finite Element Method, on probability and statistics and preferably also on scientific machine learning.
  • You have earned an MSc degree in fields like industrial engineering, applied mathematics, applied physics, mechanical engineering, civil engineering or similar.
  • Effective communication and teamwork within multidisciplinary groups come naturally to you.
  • You have experience in programming languages such as Python and C++, and are eager to hone these skills further.

This is a unique opportunity to advance both your academic and practical skills and to create a strong international network, and we encourage you to apply if you meet these criteria.    

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements .

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2770 per month in the first year to € 3539 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged. 

For international applicants, TU Delft has the Coming to Delft Service . This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme  for partners and they organise events to expand your (social) network.

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values  and we actively engage  to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Our research projects are conducted in close cooperation with a wide range of research institutions. CEG is convinced of the importance of open science and supports its scientists in integrating open science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management.

Click here  to go to the website of the Faculty of Civil Engineering & Geosciences.

For more information about this vacancy, please contact Iuri Rocha, Assistant Professor, e-mail: [email protected] .

Are you interested in this vacancy? Please apply no later than 7 January 2024 via the application button and upload:

  • Motivation letter;
  • Academic CV;
  • Your MSc thesis or a paper you wrote based on your MSc research. If neither are available in English, include a 1-page abstract of your MSc thesis written in English;
  • Transcripts from your BSc and MSc studies.    

Please note:

  • You can apply online. We will not process applications sent by email and/or post.
  • A pre-Employment screening can be part of the selection procedure.
  • Please do not contact us for unsolicited services.


Similar Positions