Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- NEW YORK UNIVERSITY ABU DHABI
- National Renewable Energy Laboratory NREL
- University of Michigan
- ETH Zurich
- Texas A&M University System
- University of West Florida
- Arizona State University
- Cornell University
- George Mason University
- Max Planck ETH Center for Learning Systems
- Oak Ridge National Laboratory
- Stanford University
- Texas A&M University
- Texas A&m Engineering
- The University of British Columbia
- University of British Columbia
- University of California, Los Angeles
- University of San Diego
- University of Texas at Austin
- 9 more »
- « less
-
Field
-
Max Planck ETH Center for Learning Systems | New Germany, KwaZulu-Natal | South Africa | 25 days ago
for Science, AI Safety, Bio-inspired Robotics, Biomechanics, Causal Inference, Computational Biology, Computer Graphics, Computer Vision, Control Systems, Deep Learning, Digital Humans, Earth
-
Geospatial Artificial Intelligence (GeoAI) under the supervision of Dr. Wenwen Li. The successful candidates will work at the intersection of cutting-edge AI (e.g., computer vision, generative AI, large
-
of performing complex tasks in strategic research clusters. The ideal postdoctoral candidate will have a strong background in robotics, computer vision, and distributed intelligent systems, with
-
Digital Health Portfolio: Contribute to projects spanning AI, digital biomarkers, wearables, computer vision, remote monitoring, precision health, and intelligent surgical systems. Unique Multimodal Data
-
advance the design and operation of next-generation thermal systems, developing strategies to model, optimize, predict, and autonomously control thermal systems behavior in complex systems. The candidate
-
ML algorithms, enhancing human-AI collaboration, and exploring systems tailored to dynamic, human-centered environments. They may also work with diverse signal modalities, including vision, speech
-
ML algorithms, enhancing human-AI collaboration, and exploring systems tailored to dynamic, human-centered environments. They may also work with diverse signal modalities, including vision, speech
-
ML algorithms, enhancing human-AI collaboration, and exploring systems tailored to dynamic, human-centered environments. They may also work with diverse signal modalities, including vision, speech
-
design and optimization for building energy-efficient and robust brain-inspired, autonomous, and cognitive systems and intelligent vision systems, including efficient learning and inference of
-
design and optimization for building energy-efficient and robust brain-inspired, autonomous, and cognitive systems and intelligent vision systems, including efficient learning and inference of