Neurocognitive ML Research Fellow for Early Learning

Nanyang Technological University; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

Nanyang Technological University (NTU), Singapore, invites applications for a Research Fellow to join an inter-disciplinary Science of Learning team spanning Psychology and the School of Physical and Mathematical Sciences. The role focuses on brain-based machine-learning models for early prediction of mathematical learning difficulties in kindergarten and early primary students.

The successful candidate will coordinate project activities, conduct data collection with children, develop ML/DL

Qualifications

• PhD in experimental psychology, cognitive psychology, educational neuroscience, AI in education, biomedical engineering, computer science or related fields.
• Experience with cognitive neuroscience or neuroimaging with children or adults, preferably using fMRI.
• Experience with cognitive/neuropsychological assessments with children and/or adults.

Responsibilities

• Conduct independent research within an inter-disciplinary Science of Learning team.
• Coordinate day-to-day project activities and ethics documents.
• Conduct literature reviews and prepare progress and policy reports.
• Supervise and lead a team of research staff and student assistants.
• Coordinate neuroimaging studies and data collection with young participants.
• Develop or manage data analysis pipelines and ML/AI approaches.
• Collaborate across disciplines and stakeholders.
• Carry additional duties as required by PI and co-PI(s).

Skills

Communication

Statistics

Coding (R, MATLAB, Python)

Project management

Leadership

Independent work

Education

PhD (doctoral degree)

Tools

SPSS

R

Jamovi

JASP

SPM

AFNI

FSL

MATLAB

Job description

Nanyang Technological University (NTU), Singapore, invites applications for a Research Fellow to join an inter-disciplinary Science of Learning team spanning Psychology and the School of Physical and Mathematical Sciences. The role focuses on brain-based machine-learning models for early prediction of mathematical learning difficulties in kindergarten and early primary students.

The successful candidate will coordinate project activities, conduct data collection with children, develop ML/DL