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

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.

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.

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.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from Promovendus gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.

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.

Additional information

If you would like more information about this vacancy or the selection procedure, please contact Farbod Alijani, via F.Alijani@tudelft.nl.