PhD Position in Visual Computing / Machine Learning
Technical University of Munich
Germany

PhD and PostDoc Positions in Visual Computing / Machine Learning (Prof. Niessner)

22.10.2020, Wissenschaftliches Personal

Topic / Area: The positions are flexible in terms of research direction within 3D vision with a heavy focus on cutting-edge deep learning-based techniques. We are particularly interested in static and dynamic 3D reconstruction, semantic scene understanding, and generative models for photo-realistic image/video synthesis. Overall, the main focus is on high-impact research with the aim to revolutionize the research field in 3D learning. Qualification: - Applicants for a PhD must hold a Master’s degree in computer science or equivalent - Fluent written and spoken English skills are essential - Proficient C++ coding skills (this is critical and will be tested) - Experience with deep learning frameworks (TensorFlow / PyTorch) - Excitement, self-motivation, and commitment to revolutionize the field - Post Doc Applicants Only: academic track record with publications at top-tier venues in computer vision, graphics, or machine learning (CVPR, ECCV/ICCV, Siggraph, Siggraph Asia, NeurIPS, ICML, ICLR) How to Apply: - Documents required: CV, research statement, BA and MA transcripts, MA thesis [optional] - Ask two recommenders who know your work to directly email recommendation letters - Send all documents (incl. rec. letters) directly to Prof. Matthias Niessner (niessner@tum.de) - Please understand that we cannot review incomplete applications TUM and VC & AI Lab: The Technical University’s computer science department is the Top-1 ranked computer science department in Germany. Our culture is highly international with over 40% international students in the MA program. The visual computing lab embraces its diverse culture, and is proud to host seven PhD students from 5 different countries and 19 Master students from 10 different countries. Our lab language is English. Website: https://niessnerlab.org/openings.html

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Kontakt: niessner@tum.de

Mehr Information

https://niessnerlab.org/openings.html


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