- Area: Deep Learning and SLAM
- Duration: 2 years (PDF); 4 years (Ph.D.)
- Starting Date: Anytime (PDF); September 1st, 2021 (Ph.D.)
- Salary: Providing a competitive salary benefit package
- Application Deadline: Until the position is filled (PDF); November 30th, 2020
Dr. Gunho Sohn’s Lab in the Department of Earth and Space Science and Engineering at York University in Toronto is opening a Postdoctoral Fellowship (PDF) and PhD positions to work on the development of deep learning methods for computer vision and SLAM. The successful candidates will work on an exciting project to develop an innovative pipeline for 3D semantic mapping and SLAM using mobile laser point clouds and images. This is a collaborative project with Teledyne Optec in Toronto (https://www.teledyneoptech.com/en/home/), the world leader in the development and manufacture of advanced fully-integrated lidar and camera solutions in ground-based, airborne and spaceborne mapping systems.
The application of deep learning to automatic generation of 3D semantic maps using the sheer amount of mobile mapping data is a rapidly growing area of research and the successful applicant will be expected to develop novel techniques to address the unique challenges posed by image understanding and mapping in complex urban environments.
Postdoctoral Fellowship: Applicants for Postdoctoral Fellowship will have demonstrated expertise in computer vision and machine learning, with particular experience and educational background in one or more of the following areas:
- Deep Learning;
- Simultaneous Localization and Mapping (SLAM); and
- 3D Computer Vision;
The candidate should have a doctorate in a relevant discipline obtained within the last five years and demonstrate a proven publication record in computer vision and machine learning research. The position is for two years. The salary for this position is competitive.
PhD Studentships: Applicants for the PhD are required to have finished, or be close to finishing, their Master’s degree or equivalent, in geomatics engineering, electrical engineering, or computer science. We prefer to invite a talented PhD applicant, with particular experience and educational background in SLAM and deep learning.
The PhD candidate should demonstrate a strong academic background (GPA of B or better) and English fluency in spoken and written (e.g., Above IELTS 7 bands). The PhD students appointed to this position will be expected to start his/her doctoral research program from September 1st, 2021.
Required Qualifications (Common): Candidates with the following qualifications are encouraged to apply:
- Proven publication record in computer/robotics vision and machine learning;
- Solid programming skills (including C++ or Python);
- Experience with one or more of TensorFlow, PyTorch and Keras is an advantage;
- Proven record of strong scientific writing skills and high motivation to produce publishable results; and
- Excellent English communication skills, both written and oral.
Research Environment: Dr. Sohn’s lab is located in Lassonde School of Engineering at York University (https://lassonde.yorku.ca/) and Centre for Research in Earth and Space Science (CRESS) (http://cress.info.yorku.ca/). Research interests include the development of 3D primitive-based urban modeling and augmentation, autonomous mapping and navigation and digital infrastructure modeling. The postdoctoral fellow will be working with faculty and graduate students in several research programs led by York University:
- NSERC CREATE Data Analytics & Visualization (https://www.createdav.com/welcome/)
- Intelligent Systems for Sustainable Urban Mobility (http://issum.yorku.ca/)
- Advanced Disaster, Emergency and Rapid Response Simulation (http://adersim.info.yorku.ca/)
- Vision: Science to Application (http://vista.info.yorku.ca/)
To Apply: Please submit a brief statement of research interests, a curriculum vitae and a list of publications and contact information for 2-3 references (not letters required at this stage) to firstname.lastname@example.org.
Information about Dr. Sohn’s lab at York University can be found at www.yorku.ca/~gsohn. Following Canadian Employment and Immigration guidelines, applicants must be eligible to work in Canada.