Research Fellow, Machine Learning and AI Modelling Techniques
The research project involves the development of digital twins for emergency medical ventilators.
The Research Fellow is expected to contribute in the following aspects:
- Develop digital twin models for ventilators using machine learning and AI modelling techniques,
- Run simulations to obtain ventilator’s performance in varying conditions,
- Draw insights through data modelling & analysis to improve ventilator’s performance (e.g. safety, reliability),
- Apply machine learning and optimization techniques on real-time usage and performance data to optimize ventilator’s performance.
- Possess a PhD degree in a relevant discipline, e.g. electrical / electronic engineering, computer engineering, computer science, mechanical engineering etc.
- Knowledge in machine learning, deep learning, optimization, control systems, IoT/sensor systems, data analytics or closely related field
- Familiar with deep learning libraries (Keras, Tensorflow, PyTorch)
- Excellent analytical and computational skills
- Software programming in Python, C++ and knowledge of Cloud Computing
- Understanding of software engineering principles
- Preferably someone who can commence work asap.
- Ability to work independently as well as part of a team with strong initiatives and have the curiosity to explore the unknown.
- Salary will be commensurate with qualifications and experience.
- Open to fixed term contract
Location: Kent Ridge Campus
Department: Electrical And Computer Engineering
Employee Referral Eligible: No