Research Associate: Vision & Causal AI

ntu; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

NTU Singapore's College of Computing and Data Science invites applications for a Research Associate to advance AI, vision-language research, and deep learning methods in real-world datasets.

The role focuses on developing visual reasoning methods for VQA, dialog, and captioning, along with causal inference tools and deep learning for computer vision in a collaborative research environment.

Qualifications

• Preferably Master in Computer Engineering, Computer Science, Electronics Engineering or equivalent
• Minimum 3 years of related work experience
• At least 1 topic-related publication in top-tier journals and conferences (CVPR, ICCV, ECCV, NeurIPS, ICML, AAAI, TPAMI, IJCV, TIP, TMM)
• Deep understanding of Machine Learning, Deep Learning, Computer Vision and Causal Inference
• Experience in at least one Deep Learning framework such as TensorFlow, PyTorch, MXNet and programming languages such as Python, Matlab, R and/or C/C++
• Demonstrated project experience in causal inference will be an advantage
• Good written and oral communication skills

Responsibilities

• Development of visual reasoning methods for vision and language (VQA, visual dialog, visual commonsense reasoning, captioning, object detection, video analysis)
• Development of causal reasoning tools (causal inference, counterfactual analysis, causal discovery)
• Development of deep learning methods on computer vision

Skills

Visual reasoning

Deep learning

Computer vision

Causal inference

Python

TensorFlow

PyTorch

Matlab

R

C/C++

Education

Master in Computer Engineering / Computer Science / Electronics Engineering or equivalent

Tools

TensorFlow

PyTorch

MXNet

Job description

NTU Singapore's College of Computing and Data Science invites applications for a Research Associate to advance AI, vision-language research, and deep learning methods in real-world datasets.

The role focuses on developing visual reasoning methods for VQA, dialog, and captioning, along with causal inference tools and deep learning for computer vision in a collaborative research environment.