Research Fellow, Machine Learning, Maritime Decarbonisation
International shipping has been increasingly committed to addressing its greenhouse gas (GHG) emissions and identifying appropriate decarbonising pathway for the industry.
Several existing studies on simulating greenhouse gas (GHG) emission projection from international shipping, predicting maritime low emission pathway, and analysing the impact on environment and economies are based on selected assumptions.
With the growing interest in GHG emission reduction, a multi-phase decarbonisation project was commissioned and we are now entering phase 3.
Part of the job scope is to build an integrated model that can:
- Estimate and model emissions and carbon intensity in international shipping
- Evaluate the global maritime transition pathways through a series of scenario
- Create future decarbonisation pathways for international shipping
Connect with us to find out more how you can make an impact to a cleaner and greener world.
Interested candidates should upload their detailed curriculum vitae giving full details of research experience and list of publications.
- A PhD in a quantitative field (e.g., Computer Science, Statistics, Engineering, Science).
- Excellent coding skills to able to solve problems in a fast pace. Familiar with popular machine learning models and eager to learn new things.
- Ability to work as a team and possess initiatives to assume responsibilities to lead the team forward.
- Must be result oriented and meet the deliverables with focus on driving impact.
Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department : Industrial Systems Engineering and Management
Employee Referral Eligible: No
Job requisition ID : 11289