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TU Delft

PhD Position Harnessing Nonlinear Dynamics: From Data-Driven Discovery to Engineering

TU Delft
3.059 tot 3.881
nieuw
Status Open
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Wat wij vragen

Opleiding
MSc university degree in physics, mechanical engineering, or mathematics. High motivation for teamwork and good communication skills. Experience in nonlinear dynamics and /or random vibrations and computational design. Good programming skills with experience in reduced-order modelling techniques and data-driven symbolic regression tools is a plus.
Ervaring
High motivation for teamwork and good communication skills. Good programming skills with experience in reduced-order modelling techniques and data-driven symbolic regression tools is a plus.
Talen
  • Je beheerst Engels

Wat wij bieden

Salaris
€ 3.059 tot € 3.881
Uren
Flexible work schedules can be arranged.
Type vacature
intern

Vacaturebeschrijving

PhD Position Harnessing Nonlinear Dynamics: From Data-Driven Discovery to Engineering Job description

Nonlinear dynamics lies at the centre of many mechanical systems, from large-scale structures to nanoscale devices. Yet, predicting and exploiting nonlinear behaviour,  particularly the role of modal couplings in energy transfer and dissipation, remains a fundamental challenge. This PhD project offers a unique opportunity to tackle these two complementary perspectives.

In the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools. The goal is to extract governing equations directly from experimental data, enabling accurate prediction of nonlinear phenomena such as modal interactions and dissipation pathways across scales: from complex structural assemblies to nanomechanical resonators.

In the second direction, you will explore the geometric design of nonlinear systems. Using nonlinear reduced order modelling (ROM) integrated with optimization algorithms, you will design structures that deliberately harness modal couplings to exhibit tailored nonlinear behaviour, with direct applications in ultrasensitive resonant sensing.

Together, these approaches will provide both data-driven methods for uncovering nonlinearities and dissipation mechanisms, and design strategies for exploiting them. The project sits at the interface of nonlinear mechanics, computational modelling, and machine learning, and is well-suited for candidates eager to develop computational frameworks for next-generation structural dynamics and nanomechanical technologies.

The vacant position is in the Dynamics of Micro and Nanosystems (DMN) group. The group focuses on exploiting dynamics to create technologies that can lead to new products in the fields of scientific instrumentation, consumer electronics and healthcare. Our research spans from measuring and manipulating materials at the micro and nano scale, to the design of world-class sensors and actuators.

You will be working in an international environment in one of the leading technical universities of Europe, with access to state-of-the art computational facilities. You will be contributing to a challenging topic related to a European Research Council (ERC) Consolidator and a Dutch Research Council (NWO) grant.

As a PhD student your tasks will include but not limit to:

  • Mastering concepts from nonlinear dynamics, data-driven model discovery, and computational design.
  • Extending our existing theoretical frameworks in investigating design optimization and nonlinear parameter estimation.
  • Contributing to the teaching and assisting with dynamics relevant courses at the department.
  • Writing a PhD thesis, publishing in renowned computational journals, and presenting your research at international meetings.
Job requirements

We are looking for applicants with the following qualifications:

  • MSc university degree in physics, mechanical engineering, or mathematics.
  • High motivation for teamwork and good communication skills.
  • Experience in nonlinear dynamics and /or random vibrations and computational design.
  • Good programming skills with experience in reduced-order modelling techniques and data-driven symbolic regression tools is a plus.
TU Delft (Delft University of Technology)

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!

Faculty Mechanical Engineering

From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It's a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME's outstanding, state-of-the art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well?'being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.

to go to the website of the Faculty of Mechanical Engineering. Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work.

Conditions of employment

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.

, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.

Are you interested in this vacancy? Please apply via the application button and upload the following documents:

  • Motivation letter.
  • MSc transcript.

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.

Please note:

  • You can apply online. We will not process applications sent by email and/or post.
  • As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
  • Please do not contact us for unsolicited services.
Salarisomschrijving

Het salaris bedraagt €3059 - €3881

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