About the Company: The Centre of Excellence in Maritime Safety (CEMS) is a national R&D centre, a collaborative effort between Singapore Polytechnic and the Singapore Maritime Institute. We focus on developing innovative digital solutions and training systems to enhance maritime safety. Our state-of-the-art research facilities, including South-east Asia's first navigation research simulator, setting us as an innovation champion.
About the Role: You will join CEMS's expanding AI team to develop machine learning models, build analytics capabilities, and conduct data science studies. Your work will enhance simulator training and assessment, drive innovation in training methods, and support the development of digital technologies for the maritime industry.
Responsibilities
- Applied AI Development: Design and implement AI/ML models to address complex safety, training, and operational challenges, with a focus on areas such as clustering, anomaly detection, trajectory prediction, and probabilistic modeling.
- Spatiotemporal Analytics: Develop methods for analyzing large-scale data that combine spatial and temporal dimensions, including identifying patterns, hotspots, and emerging risks in dynamic environments.
- Research & Prototyping: Explore and validate advanced AI techniques (e.g., unsupervised learning, Bayesian methods, sequence models) to capture uncertainty and model human or system behaviors.
- Collaboration: Work closely with internal teams and external partners to co-develop AI solutions and integrate them into simulation, training, or decision-support platforms.
- Project Execution: Lead AI components within R&D projects, ensure timely delivery of milestones, and produce documentation for both technical and non-technical stakeholders.
- Knowledge Sharing: Present findings, models, and results to management and industry collaborators, contributing to organizational strategy and technology adoption.
Qualifications
- Degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- Strong foundation in probabilistic modeling, clustering, anomaly detection, trajectory forecasting, and spatiotemporal data analysis.
- Demonstrated ability to apply these techniques to real-world datasets in any domain, with exposure to areas such as simulation, transportation, or geospatial/temporal data preferred.
- Proficiency in Python, with experience in ML frameworks such as PyTorch or TensorFlow.
- Solid background in statistical analysis, algorithm design, and working with large datasets.
- Strong problem-solving ability, project execution skills, effective communication, and the capacity to mentor or support junior team members.
- Knowledge of Computer Vision (CV), Natural Language Processing (NLP), and/or Large Language Models (LLMs) is a plus.
Only shortlisted candidates will be contacted, and successful candidate will be offered a 2-year contract in the first instance.
