PhD student in Digital Fabrication of Curved Geometries in Zürich

We will develop novel methods to generate sensors that conform to curved surfaces, thereby imbuing real-world objects with the capability of becoming sensitive to user input to inform novel smart embedded devices that can adapt to user behavior during direct and indirect use. We will develop methods in fabrication, geometric processing, and surface parametrization as well as simple methods to detect and infer understand user input.

At the  (Prof. Christian Holz), we are looking for a PhD candidate with an interest in conducting cutting-edge research, with a strong motivation to work on challenging topics, and a strong desire to learn.

Many sensing approaches today are limited to planar surfaces, such as those on touchscreens, tables, displays, or walls. While cameras allow surfaces with arbitrary shape to become sensitive to user input, they are limited to stationary settings. A large challenge is in creating sensor surfaces that are non-planar, while keeping parametrization properties that can be defined depending on intended use. Therefore, we require novel geometric processing methods to develop surface-conforming overlays that integrate sensor configurations known from planar setups. This will enable arbitrary objects to be augmented with the functionality that has become common in smart devices.

You should have a background in Computer Science, Electrical Engineering, Robotics, Bioengineering, or Mechanical Engineering. Most importantly, you should have experience in:

• digital fabrication or 3D graphics, including experience with
  • geometric modeling, discrete curvature, and geometry processing (required)
  • recommended: prior work with non-planar surfaces
  • fabrication using laser cutting and 3D printing
  • experience with 3D graphics programming
• work with sensing on surfaces (e.g., for user input or the like), such as
  • capacitive sensing
  • resistive sensing
  • optical sensing, detecting user input using image processing or applied computer vision
• optional but useful: experience with electronics
  • evaluate hardware datasheets and schematics
  • design PCB layouts using ECAD tools
• optional: experience with machine learning basics and
  • an understanding of underlying deep learning and machine learning concepts
  • experience in common toolkits, e.g., TensorFlow, PyTorch

If available, you should include a link to their portfolio, such as

• a website showing past projects (, ) or
• link to a Github profile, blog, etc.

• an excellent master's degree (M.Sc., M.Eng. or equivalent) in Electrical Engineering, Computer Science, Robotics, or closely related
• written and spoken fluency in English
• demonstrated experience working with digital fabrication
• experience implementing research prototypes
• strong interpersonal and communication skills
• recommended: demonstrated international mobility through exchanges, internships, or visits at other research institutions

Prior experience in conducting research is a plus including prior publications.

We offer an exciting and stimulating environment to study and work in. In the SIPLAB at ETH Zürich, we are an international and cross-disciplinary research group working across computational interaction, physical computing, applied computer vision, wearable sensing, and mobile health. We bring together skills and experience from Computer Science, Electrical Engineering, Robotics, Mechanical Engineering, and Biomedical Engineering. We are part of the Department of Computer Science and affiliated with the Department of Information Technology and Electrical Engineering and collaborate with several other research groups, which are internationally recognized in computer graphics, fabrication, sensing systems, and machine learning. We also collaborate with several other institutions and companies in Switzerland and abroad. We publish our research at the top venues in technical Human-Computer Interaction, Ubiquitous Computing, Graphics, and Computer Vision.

For an overview of our projects, learn more about: