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Jobs in Singapore   »   Jobs in Singapore   »   Education / Training Job   »   Research Assistant, Explainable AI (School of Computing)
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Research Assistant, Explainable AI (School of Computing)

National University Of Singapore

National University Of Singapore company logo

Interested applicants are invited to apply directly at the NUS Career Portal

Your application will be processed only if you apply via NUS Career Portal

We regret that only shortlisted candidates will be notified.

Job Description

National University of Singapore (NUS) is a reputed world-class university that provides an outstanding and supportive research environment. Singapore is a vibrant, well-connected city with low taxes and research hub in Asia.

The National University of Singapore invites applications for the position of Research Assistant in the Department of Computer Science, School of Computing (SoC). We are a team of researchers passionate about building human-centered explainable AI techniques.

The Research Assistant will be responsible for working closely with the Principal Investigator and other lab members on one or more research projects. He/she should have hands-on experience in artificial intelligence and machine learning.

Key responsibilities:

  • Discuss and collaborate with other team members to advance the research project.
  • Design and implement the explainable machine learning models.
  • Analyze data based on the evaluation results.
  • Provide feedback on any problems encountered.

Qualifications

  • Bachelor's or master's degree in computer science with a focus in AI/machine learning. Relevant research experiences would be a plus.
  • Strong coding and problem-solving skills with experience in Python. Familiar with the machine learning frameworks (e.g. Scikit-learn, PyTorch) and data analysis tools would be a plus.
  • Ability to read and understand methodologies in research papers.
  • Good written and verbal communication skills in English. for reporting issues during experiments and involving in discussions on model design.

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