PhD in AI-Assisted Generative Design and Optimization of Compliant Mechanisms (F/M/D) 100%

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Your mission

Help create engineering tools that combine mechanics, computation and AI to make the design of complex optomechatronic systems faster, more rigorous and more reusable.

CSEM and the Engineering Design and Computing Laboratory of ETH Zurich are seeking a doctoral researcher to investigate how AI and advanced computational methods can support the design of high-performance optomechatronic systems. The research will be anchored in compliant mechanisms, where performance emerges from tightly coupled geometry, material behaviour, actuation, sensing, manufacturing and verification constraints.

The ambition is not to treat AI as a black box. You will develop scientifically grounded methods that combine physics-based models, simulation data and engineering knowledge, and make their predictions traceable, testable and useful to engineers.

Research focus

The project will build on the outcomes of Guilain Lang’s doctoral thesis, “CAM2S2: Compliant AM Mechanisms Systematic Synthesizer” [link to the online thesis], which established a systematic framework for translating requirements into compliant mechanism designs. The new research will critically assess and selectively reuse, adapt or extend the methods and tools developed in CAM2S2, without being constrained to retain the complete existing framework. From this foundation, it will develop and validate AI-assisted methods to derive engineering insight from requirements, explore broader solution spaces and generate reusable models for mechanism and optomechatronic system design. The exact thesis plan will be refined with the selected candidate and the supervisors. Expected research directions include:

•  AI-assisted interpretation of requirements to identify critical design drivers, sensitive parameters, conflicts and uncertainty.
• Traceable use of scientific literature and CSEM engineering knowledge, including models, test results and lessons learned.
• Inverse and multi-objective design methods that connect required performance to geometry, material choices and manufacturing constraints.
• Extension of mechanism synthesis beyond a limited set of topologies, including variable cross-sections and alternative manufacturing routes.
• Physics-informed surrogate or reduced-order models for nonlinear stiffness, stress, buckling, parasitic motion and other key mechanism properties.
• Automated generation of design-of-experiment campaigns using finite element models and computational methods.
• Implementation and validation on representative compliant optomechatronic demonstrators.

Your responsibilities

• Formulate focused research questions and establish robust benchmarks against existing analytical, numerical and engineering workflows.
• Develop computational models, algorithms and research software with reproducible data and version control practices.
• Design numerical experiments, critically assess model validity and quantify uncertainty.
• Work closely with specialists in mechanics, systems engineering, AI, manufacturing and testing at CSEM and ETH Zurich.
• Validate selected methods against higher-fidelity simulations and experimental evidence.
• Publish in leading peer-reviewed journals and present at international conferences.
• Transfer useful methods into engineering tools and document them so that they can be understood, challenged and reused.

Your profile

Know-how

Essential

• Master's degree in microtechnology or mechanical engineering, computational science and engineering, applied mathematics, computer science, robotics, or a closely related field.
• Strong foundation in computational mechanics and numerical modelling, including finite element analysis.
• Understanding of the architecture and development of multidisciplinary optomechatronic or mechatronic systems.
• Ability to develop and assess computational methods beyond routine use of commercial FEM software.
• Solid programming skills, preferably in Python and/or a compiled scientific-computing language.
• Rigorous scientific reasoning, independence, creativity and the ability to communicate clearly in written and spoken English.

Strong assets

• Background in machine learning, artificial intelligence or data-driven modelling, with the ability to connect these methods to physical systems.
• Experience with surrogate modelling, optimisation, uncertainty quantification, reduced-order modelling, differentiable simulation or scientific machine learning.
• Knowledge of compliant mechanisms, nonlinear structural mechanics, mechanism synthesis or topology optimisation.
• Experience in design for additive manufacturing. DfAM is a major advantage, although the project will not be limited to DfAM.
• Experience with CAD, parametric modelling, experimental validation or the development of engineering software.

What we offer

• A doctoral project at the intersection of fundamental methods and demanding real-world engineering applications.
• Joint supervision by CSEM and Prof. Kristina Shea's Engineering Design and Computing Laboratory at ETH Zurich.
• Access to CSEM expertise, engineering heritage, simulation and test capabilities in precision mechanisms and optomechatronic systems.
• A multidisciplinary and international environment with close interaction between researchers and engineers.
• The opportunity to publish scientific results while translating research into methods with direct engineering value.
• Attractive employment conditions and access to the ETH Zurich doctoral ecosystem, subject to the applicable admission and approval requirements.

CSEM mission and values

We believe in a future that is imagined, shared, and created TOGETHER.

At CSEM, we don’t just innovate, we envision technologies that shape tomorrow.

Since 1984, our purpose has remained constant: to harness science and technology for the benefit of society. Today, with over 650 people across Switzerland, we remain committed to our mission: to develop and transfer world-class solutions in digital technologies, precision manufacturing, and sustainable energy systems. In doing so, we empower Swiss industry to innovate and remain competitive.

Our strength is the excellence of our people. We believe that strong values support the successful development of our organization as well as the harmonious and balanced development of all our employees.

We are

• A unique place between research and industry at the cutting edge of new technologies
• An innovative, non-profit, and employee-driven company
• A dynamic, multidisciplinary, and multicultural environment
• A solar team focused on enabling solutions to energy challenges for a sustainable world

Working@CSEM means

• Being part of a passionate community
• Flexibility, attractive working conditions, and opportunities of development
• Benefit from a management style based on trust & feedback

Please submit:

• A concise motivation letter explaining why this research question fits your background and ambitions.
• Curriculum vitae, degree certificates and academic transcripts.
• A short description or link to one research, software or engineering project that best demonstrates your contribution.
• Contact details for at least two academic or professional referees.

In your motivation letter, briefly describe how you would combine physics-based simulation and machine learning without losing traceability or engineering credibility.

Applications will be evaluated on scientific potential, depth of fundamentals, evidence of independent problem solving and alignment with the interdisciplinary nature of the project. The position will be primarily based at CSEM in Neuchâtel. The doctoral researcher will also spend time at ETH Zurich, as required by the research activities and the collaboration with the Engineering Design and Computing Laboratory. The project will be conducted as an external doctoral thesis under ETH Zurich supervision, subject to formal university approval and admission.

 

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity.

 

We look forward to receiving your complete application file via (CV, cover letter, certificates & diplomas) our job page. Preference will be given to professionals applying directly.