Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
operation and maintenance of equipment Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer
-
. Strong programming skills in Python and experience with software development for data analysis or machine learning applications. Knowledge of artificial intelligence and machine learning techniques
-
Engineering, Computer Science, Robotics, or a closely related discipline, with foundational knowledge in signal processing and machine learning. Working knowledge of computer vision and deep learning concepts
-
problems or enhance industry operations Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI
-
imaging, biomedical instrumentation, image processing, or machine learning is desirable. Ability to follow technical protocols accurately and to maintain clear documentation of work performed. Ability
-
) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5, Franka Emika
-
degree in Computer Science/Computer Engineering. Possessing a Master’s or PhD degree will definitely be advantageous. Knowledge of machine learning, pytorch, huggingface etc... Knowledge of image
-
at NTU are looking for a Research Fellow (RF) to carry out research in probabilistic machine learning and GenAI, by exploring cutting-edge approaches such as sequence model design, continual learning
-
learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP, BLIP) or scene-graph inference is a plus. Key Competencies Strong software development and debugging skills. Able
-
, entomology and machine learning. This position will contribute to One Health epidemiology research projects on the impacts of environmental change on vector-borne and zoonotic disease risks. This work will