Research Fellow (Computer Engineering/Computer Science/Applied Mathematics)

NANYANG TECHNOLOGICAL UNIVERSITY; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

A leading global university in Singapore is looking for a researcher to focus on scalable machine learning systems and AI-for-Science software development. Candidates should hold a PhD in relevant fields and possess strong programming skills in Python, along with experience in machine learning and software engineering. The role involves collaboration with local clinicians and researchers on real-world applications, alongside publishing in top-tier conferences. Excellent communication and teamwork skills are essential.

Qualifications

• Strong academic background in machine learning and convex optimization.
• Prior experience in federated learning, edge computing, or healthcare applications is highly desirable.
• Demonstrated ability to formulate hypotheses and design effective experimental plans.

Responsibilities

• Conduct research on scalable machine learning systems.
• Develop software prototypes for AI-for-Science systems.
• Collaborate with local hospitals on real-world deployment.

Skills

Machine learning expertise

Proficient in Python

Strong communication skills

Software engineering

Cloud computing systems

Education

PhD in Computer Engineering, Computer Science, Applied Mathematics or equivalent

Tools

PyTorch

TensorFlow

Job description

Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a leading college that is known for its excellent curriculum, outstanding and impactful research, and world-renowned faculty.

A hot bed of cutting-edge technology and groundbreaking research, the College aims to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow’. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing.

Key Responsibilities:

• Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods.

• Develop software prototypes for AI-for-Science systems tailored to scientific discovery and data constraints (e.g., privacy, intermittency, heterogeneity of health data etc).

• Collaborate with clinicians and researchers from local hospitals (e.g., TTSH) on real-world deployment in ambient intelligence and clinical informatics.

• Publish in top-tier conferences and journals (e.g., NeurIPS, ICML, IEEE TPAMI, JBHI).

• Support the supervision of graduate students and contribute to the research group’s collaborative activities.

Job Requirements:

• Preferably PhD in Computer Engineering, Computer Science, Applied Mathematics or equivalent.

• Strong software background in software engineering and cloud computing systems.

• Strong academic background in machine learning and convex optimization.

• Prior experience in federated learning, edge computing, or healthcare applications is highly desirable.

• Proficient in Python and ML frameworks such as PyTorch or TensorFlow.

• Independent, highly analytical, proactive and a team player

• Excellent teamwork and verbal, written communication skills

• In-depth knowledge relevant to the project domain.

• Demonstrated capability to conduct innovative research.

• The ability and willingness to adapt and incorporate novel ideas and approaches in scientific industry.

• Demonstrated ability to formulate hypothesis and design effective experimental plans.

• Ability to design and implement new experimental methods.

• High-level expertise in the required experiment or modelling methods.

• Ability to initiate collaboration research in multidisciplinary team.

• Oversee and report project progress.

• Ability and willingness to work some flexible hours.

We regret that only shortlisted candidates will be notified.