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Research Associate (Large Vision Language Models)

Nanyang Technological University Singapore

SingaporeFull-timeOn-site

Posted 29 May 2026

About this role

Conduct research on adversarial attack and defense mechanisms to improve the robustness of Large Vision Language Models. Manage the research codebase and author technical manuscripts and academic publications for funding reviews. Requires a Master's degree in Computer Science, Electrical Engineering, or a related field with a research background in Deep Learning and LLMs. Proficiency in Python and PyTorch is essential, with experience in model benchmarking and optimization.

What they're looking for

PythonPyTorchC/C++Deep LearningNatural Language ProcessingLarge Language ModelsLarge Vision Language ModelsModel Benchmarking

Frequently asked questions

What does a Research Associate (Large Vision Language Models) at Nanyang Technological University Singapore do?

Conduct research on adversarial attack and defense mechanisms to improve the robustness of Large Vision Language Models. Manage the research codebase and author technical manuscripts and academic publications for funding reviews. Requires a Master's degree in Computer Science, Electrical Engineering…

What skills does this Research Associate (Large Vision Language Models) role need?

Key skills for this role include Python, PyTorch, C/C++, Deep Learning, Natural Language Processing, Large Language Models.

How much does a Research Associate (Large Vision Language Models) at Nanyang Technological University Singapore pay?

The employer did not list a salary for this role. Most similar Singapore roles publish their band on the job page.

Is this Research Associate (Large Vision Language Models) role remote, hybrid, or on-site?

This role is on-site, based in Singapore.

How do I apply for this Research Associate (Large Vision Language Models) role?

You can apply directly on Nanyang Technological University Singapore's careers page. ApplyLah can tailor your résumé and cover letter to this exact role in seconds first.