PhD Positions (TV-L E13, 100%)
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Neural Rendering, 3D Reconstruction, SLAM / Pose Tracking, Semantic Scene Understanding, Face/Body Tracking, Non-Linear Optimization, Media Forensics / Fake News Detection
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Qualification | Masters degree; expert level coding skills; strong background in numerical optimization; good english skills are essential; some set of vision / graphics / ML lectures; knowledge in scripting languages that are used by modern deep learning frameworks (e.g., Lua or Python) is a plus; extremely high motivation and dedication |
Description | We are looking for outstanding PhD candidates who are enthusiastic about cutting edge research in computer vision, computer, graphics, and machine learning. The Visual Computing Group focuses on digitizing of real-world environments and semantic scene understanding. This area is heavily inspired by the rapid progress of range sensing technology, such as the Microsoft Kinect and LIDAR systems, as well as the availability of large data collections. In addition to this newly-available sensor technology, algorithmic advances in deep learning and convolutional neural networks open up a wide range of opportunities for cutting-edge research in static and dynamic 3D capture; for instance, targeting applications in virtual and augmented reality. |
Contact | Via Application Form   |
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Documents | CV; Transcripts (BA & MA); Short Research Statement; ask two references for recommendation letters to support your application |
PostDoc Positions (TV-L E13/14, 100%)
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Neural Rendering, 3D Reconstruction, SLAM / Pose Tracking, Semantic Scene Understanding, Face/Body Tracking, Non-Linear Optimization, Media Forensics / Fake News Detection
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Qualification | PhD degree; track record at top-tier venues (CVPR, SIGGRAPH, ECCV, ICCV, NIPS); expert level coding skills; profound knowledge of numerical optimization; extremely high motivation and dedication |
Description | We are looking for exceptional PostDocs in computer vision, computer, graphics, and machine learning. The Visual Computing Group focuses on digitizing real-world environments and semantic scene understanding. This area is heavily inspired by the rapid progress of range sensing technology, such as the Microsoft Kinect and LIDAR systems, as well as the availability of large data collections. In addition to this newly-available sensor technology, algorithmic advances in deep learning and convolutional neural networks open up a wide range of opportunities for cutting-edge research in static and dynamic 3D capture; for instance, targeting applications in virtual and augmented reality. |
Contact | Prof. Dr. Matthias Nießner   |
Extra | ![]() |
Documents | CV; Transcripts (BA & MA); Short Research Statement; ask two references to directly email recommendation letters to support your application |
Master Theses
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Deep Learning, 3D Reconstruction, and Visual Computing
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Qualification | only for TUM students; expert level coding skills; strong background in numerical optimization; knowledge in scripting languages that are used by modern deep learning frameworks (e.g., Lua or Python) is a plus; extremely high motivation and dedication |
Description | We offer a variety of master theses, including "Semantic Understanding of 3D Scenes with Deep Learning", "Robust 3D Reconstructions with Commodity RGB-D Sensors", "Face Tracking and Facial Reenactment", "A Language for Non-Linear Least Squares Optimization in Graphics and Vision". If you are interested and highly-motivated to work at the intersection of computer vision, computer, graphics, and machine learning, please send us an email! |
Contact | Prof. Dr. Matthias Nießner   |
Extra | |
Documents | CV; Transcripts (BA & MA) |