Postdoctoral Scholarship Job Posting – USP – Multimodal Urban Sensing
University of São Paulo (USP) – Synestech.AI
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Project Title: Multimodal Urban Sensing
Start Date: July 2024
Supervisor: Dr. Roberto M. Cesar Jr.
Project Summary:
Urban environments are brimming with data captured by an ever-expanding array of multimodal sensors. Cameras, LiDAR, microphones, and environmental sensors paint a rich picture of city dynamics. This project fuses this diverse data using machine learning to gain a deeper understanding of urban life. We explore applications in areas like traffic management, public safety, environmental monitoring, and urban planning.
Specific Research Areas:
- Develop computer vision algorithms to analyze public datasets (e.g., SideSeeing, StreetAware).
- Implement edge computer vision for real-time analysis on sensor hardware.
- Design privacy-preserving solutions like anonymization and federated learning.
- Develop strategies for multimodal data acquisition at specific locations (e.g., bus stops).
- Craft methods to extract insights and identify critical events from sensor data.
Candidate Qualifications:
- PhD in Computer Science, Engineering, Physics, Math, or a related field.
- Strong background in mathematical modeling and programming.
- Research experience and publications in image processing, computer vision, machine learning, natural language processing or other AI fields.
- Excellent English communication skills (written and oral).
About the Fellowship:
The selected candidate will receive a FAPESP fellowship with potential benefits including:
- Part of the FAPESP Thematic Project Learning context rich representations for computer vision
- Initial funding for 2 years
- Annual fellowship of R$110K
- Overhead for travel expenses (conferences)
- Moving assistance (including flight tickets to São Paulo)
More information on FAPESP: http://www.fapesp.br/en/5427
About USP:
The University of São Paulo, a prestigious institution and top-ranked university in Latin America, boasts a Computer Vision Group at IME-USP with over 20 years of experience in machine learning research and strong international collaborations.
Application Process:
Please submit the following documents to thematic.data.science.usp@gmail.com with the subject line “Application to Postdoctoral Scholarship on Multimodal Urban Sensing”:
- Curriculum Vitae
- Link to ORCID, Google Scholar, or ResearcherID
- Summary of doctoral thesis and other relevant works
- Two recommendation letters
Application Deadline: June 10th, 2024
References related to the project
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
- Hosseini, M., Sevtsuk, A., Miranda, F., Cesar Jr, R. M., & Silva, C. T. (2023). Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery. Computers, Environment and Urban Systems, 101, 101950.
- Li, C., Jiang, X., Jiang, H., Sha, Q., Li, X., Jia, G., … & Zheng, J. (2022). Environmental Controls to Soil Heavy Metal Pollution Vary at Multiple Scales in a Highly Urbanizing Region in Southern China. Sensors, 22(12), 4496.
- Liu, Y., Zhang, Y., Wang, Y., Hou, F., Yuan, J., Tian, J., … & He, Z. (2023). A survey of visual transformers. IEEE Transactions on Neural Networks and Learning Systems.
- Piadyk, Y., Rulff, J., Brewer, E., Hosseini, M., Ozbay, K., Sankaradas, M., … & Silva, C. (2023). StreetAware: A High-Resolution Synchronized Multimodal Urban Scene Dataset. Sensors, 23(7), 3710.
- Silva, C. T., Freire, J., Miranda, F., Lage, M., Doraiswamy, H., Hosseini, M., … & Cesar Jr, R. M. (2019). Integrated Analytics and Visualization for Multi-modality Transportation Data.
- Tokuda, E. K., de Arruda, H. F., Domingues, G. S., Costa, L. D. F., Shibata, F. A., Cesar-Jr, R. M., & Comin, C. H. (2023). Estimating the effects of urban green regions in terms of diffusion. Environment and Planning B: Urban Analytics and City Science, 50(4), 1023-1038.
- Yu, M., Chen, X., Zhang, W., & Liu, Y. (2022). AGs-Unet: Building Extraction Model for High Resolution Remote Sensing Images Based on Attention Gates U Network. Sensors, 22(8), 2932.
- Zaitunah, A., Silitonga, A. F., & Syaufina, L. (2022). Urban greening effect on land surface temperature. Sensors, 22(11), 4168.