We are looking for motivated researchers to start as PhD candidates in the Computation, Appearance and Manufacturing Group. The candidates will research the exiting and rising field of computational fabrication. The group has a particular focus on appearance manufacturing using a wide range of advanced manufacturing devices, among which 3D printers. Appearance manufacturing concerns the fabrication of objects with a given appearance and has a variety of applications from medical devices and cultural heritage preservation to stop-motion animation. We have a keen interest in using deep learning methods in the context of manufacturing, where we have taken successful initial steps.
Strong applicants with a master degree (or those approaching the end of their master studies) in computer science or a related field are encouraged to apply. Research background in one or more of the following areas is necessary: computer graphics (with a preference for rendering), geometry processing, image processing or machine learning. Solid skills in mathematics and related programming languages (C++ / Python / OpenGL) are also required.
Please send your application or any inquiry to Dr. Vahid Babaei (email@example.com). The application should include a CV, motivation letter and copy of transcripts. Names of two references agreed to write a recommendation letter are also required.
The Computation, Appearance and Manufacturing group focuses on inventing new computational tools that release the full potential of advanced manufacturing processes, such as additive manufacturing (also known as 3D printing). With the immense growth of the manufacturing hardware in resolution, scale and speed, the algorithm complexity increases even more dramatically. We therefore aim at developing hardware-aware, scalable algorithms for advanced manufacturing.
The group has a particular interest in visual appearance of objects and strives for better algorithms that help creating products with novel and useful appearance characteristics using advanced manufacturing tools. The computational methods used by the group are largely inspired by a range of disciplines, from computer graphics and computational imaging to signal processing and machine learning.
For many products, their appearance is as important as their functional goal, sometimes their sole function. In our group, we focus on developing appearance reproduction workflows for a variety of hardware and processes. In case of multi-material 3D printers, given a physical object, we try to find an efficient and accurate mapping from its appearance to the digital arrangement of printer's materials, such that the printed copy is as perceptually close as possible to the original one. The results of this research will immediately enable numerous applications in rapid prototyping and manufacturing of end-use products. This spans several application domains from medical devices and surgical training, to cultural heritage preservation and anti-counterfeiting.
Computational Fine Art Reproduction
Fine art objects are instruments for aesthetic contemplation as well as social scientific studies. Fine art artifacts are exposed to different dangers, such as aging and destruction even though they already incur huge costs to museums for conserving them. It is essential that we learn to preserve this heritage for ourselves and future generations. We take fine art as a primary case study for our research on appearance reproduction. Beside the importance for cultural heritage preservation, work of art is an ideal case study since all elements of appearance are present: 3D texture, spectral color, gloss and translucency. In our group, we pursue the simultaneous modeling and replicating of these appearance attributes -- a complex, unsolved problem.