REQUIREMENTS:

PhD or Master’s degree in a quantitative field (Biomedical Engineering, Mathematics, Statistics, Computer Science, Physics, or related).
1-3 years of relevant working experience in AI development.
Solid background in computer vision, deep learning, and image/signal data analysis algorithms, LLM etc.
Hands-on experience with Python and major deep learning frameworks (TensorFlow, PyTorch, Keras etc.) and LLM.
Strong communication skills to explain technical insights to both technical and non-technical stakeholders.
Ability to work independently within a regulated development environment.
Model performance evaluation, deployment and lifecycle management experience (MLOps, monitoring, data drift management).
The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.
Type of Employment : Full-Time
Minimum Experience : 1 Year
Work Location : Biopoli

Analyze healthcare related data for diagnostic applications; define problems, hypotheses, and analysis plans.
Develop and optimize algorithms for data analysis, synthesis, visual recognition, object detection, segmentation, and signal pattern recognition etc.
Design, execute, and document experiments to validate model assumptions and performance in line with regulatory requirements.
Conduct feasibility studies and support clinical/analytical validation of AI-based algorithms for diagnostic use.
Explore and integrate cutting-edge technologies into diagnostic AI solutions, ensuring robustness, reproducibility, and compliance.
Translate business and clinical needs into data science projects, aligned with regulatory and quality management requirements.
Collaborate with cross-functional teams (R&D, QA/RA, clinical affairs) to ensure AI models meet regulatory standards. (FDA, CE-IVDR, HSA, etc.).
Prepare and maintain technical documentation, including design history files (DHF), risk assessments, software development lifecycle (SDLC) deliverables, and regulatory submissions.
Support model deployment under SaMD frameworks, including monitoring, version control, and post-market surveillance.
Stay current with trends in AI for diagnostics, SaMD regulatory guidance, and international standards.