Research Officer, A*SRL - Part-Time

The A*STAR Skin Research Labs invites applications for a Research Officer to support ongoing research in the Digital Twin of Skin (DTS) and optical metrology of Asian skin. The Research Officer will contribute through computational, analytical, and data-driven work and will be an integral member of a multidisciplinary research team.

Our research adopts a multi-scale view of skin as a complex biological system, spanning nano, micro, and meso scales. At the nanoscale, this includes molecular and cellular information such as DNA, RNA, proteins, and other biochemical constituents, while higher scales capture tissue structure, optical properties, and functional responses. These layers are integrated within the Digital Twin of Skin, which combines biophysical modelling with state-of-the-art AI techniques.

The Research Officer will work closely with the research team and senior researchers, providing computational and analytical support at the interface of physics, biology, and machine intelligence.

Roles and Responsibilities

• Support research activities through computational modelling, AI-based analysis, and data interpretation.
• Perform data processing, statistical analysis, and visualisation using appropriate software tools.
• Assist in the integration of multi-scale datasets within the Digital Twin of Skin framework.
• Collaborate with team members across disciplines to support research objectives and deliverables.

• Contribute to technical documentation, internal reports, presentations, and scientific publications.

Requirements:

• Bachelor’s or Master’s degree in Computing, Computational Science, Biotechnology, Biomedical Engineering, or a related discipline.
• Knowledge of data analysis, AI/ML, or computational modelling methods.
• Hands-on experience with MATLAB and Python; familiarity with AI frameworks such as PyTorch or TensorFlow.
• Strong analytical skills, attention to detail, and ability to follow structured research workflows.
• Ability to work independently and collaboratively within a team-based research environment.
• Curiosity and willingness to learn and apply new biophysical and computational modelling approaches.