About the role
igence in Engineering Design. The general area of the laboratory covers the study of Artificial Intelligence and Machine Learning in the context of Engineering Design problems in domains such as healthcare, power generation, aerospace, and robotics. The successful candidate will join a growing research team and have a unique professional development opportunity to help shape and guide initial investments in laboratory infrastructure.
Project background
This postdoctoral position would build upon the laboratory's past and ongoing work within the context of AI-Guided Design, Inverse Design, and Optimization across different application domains. In particular, we are developing scientific research infrastructure to better study the integration of physical experiments with digital/AI-driven models, as well as formal verification of software systems. This builds upon our existing work in automated robotic assembly , automated design for assembly , and benchmarking within this domain, such as through our EngiBench and EngiOpt libraries. We would also be interested in developing new work in formal verification of mechanical systems, such as through Autoformalization and techniques from Program Analysis or Synthesis. This position would involve helping set the vision and scientific direction for future efforts in this broad area, and collaborating with Ph.D. students, other Post-Docs, and Scientific Staff (such as software developers from ETH's Scientific IT Services group) to produce world-class and high-scientific-value research publications and infrastructure. As such, it offers unique and advanced professional development opportunities in both leadership and laboratory operations that will position the candidate well for future success as an independent scientist. You can read more about our work at our laboratory's current website .
Job description
As a postdoctoral researcher, your responsibilities may include
Development or analysis of novel algorithms for engineering design applications, such as Inverse Design, Generative Modeling, Robotics, or Program Analysis/Synthesis
Collaborating with partners from industry, academia, and national labs to translate real-world needs into scientific or technical questions
Working independently or with other scientific staff to deploy our simulation or algorithm pipelines on advanced computational infrastructure, such as High-Performance Computing systems
As many of the lab members actively contribute together to various projects, this role will involve some project management and coordinating the development efforts of others
Helping to set up the specialized digital or physical scientific infrastructure needed to support planned experiments as relevant to your research focus. This also includes participating as an active lab citizen in improving lab-wide infrastructure via participation and planning for lab retreats or helping set up lab-wide processes
Participating actively in dissemination efforts such as publishing scientific papers, attending or presenting at conferences or workshops, or other scholarly outreach activities
Designing and writing portions of scientific research proposals to support laboratory efforts
Supervising Ph.D., Masters, and/or Bachelors students
Limited assistance in teaching efforts for relevant coursework in the laboratory's general area
Profile
The following are some characteristics that may align well with the position
You should be a highly motivated researcher with expertise in some of the following areas (please address these in the cover letter): Machine Learning, Robotics, Computational Simulations (e.g., CFD, FEA, etc.), domain expertise in Engineering problems, familiarity with High-Performance Computing (HPC) environments, or Software Engineering/Analysis
You are expected to have a Ph.D. degree in Mechanical Engineering, Computer Science, Applied Mathematics, Robotics, or related fields with an excellent track record in archival journal publications, Computer Science or Robotics conferences, or other competitive venues appropriate to your Ph.D. research field
You should possess strong English language skills and the ability to work successfully and collaboratively on diverse, multinational teams
Preferred candidates will also possess working knowledge of recent Machine Learning or HPC-related techniques or tooling, such as relevant Python or Julia libraries (PyTorch, JAX, and similar) and how to package simulation or ML codes for dissemination or deployment on distributed servers (e.g., Docker, Singularity, and running codes on HPC clusters)
Knowledge of (or prior research) in robotics (e.g., ROS, forward/inverse kinematics), manufacturing (e.g., Additive or Subtractive Manufacturing), or computer vision/language models is a plus, but not a requirement
Experience in Formal Verification and Software Analysis is also highly desirable
Preferred candidates will know professional software development practices, such as Code Quality measures, distributed development workflows (e.g., using Git/GitHub), such that you can lead a group with multiple contributors and maintain high levels of code quality
This position involves s