Research Assistant in Energy Studies Institute (Automated Carbon Solution)
National University of Singapore
- Singapore
- Permanent
- Full-time
- To report on the motivations for and expanded adoption of socially responsible business practices, including carbon disclosure, either voluntarily or in response to evolving national/international policies e.g. reacting to carbon tax revisions or similar;
- To review regional and international best practices for carbon footprint measurement, including the scope of emissions captured;
- To work with the hybrid infrastructure management platform as the central component of a high-integrity automated carbon footprint management solution;
- To explore the potential for, and possibly support the development of, a digital twin architecture built upon a hybrid infrastructure management platform, as a framework for strategic evaluation of carbon footprint policies;
- To support the development of virtual reality software for integrated simulation extensions of the hybrid infrastructure management platform output, accessed through APIs;
- To explore the development of a lifecycle management algorithm around the critical elements of data centre cooling system architecture(s);
- To document case studies for the use of automated technologies in carbon emissions data capture, verification and/or reporting.
- Possess a Bachelor's degree in computer science, data science or other related fields
- Applicants should have demonstrated experience in quantitative data analysis.
- Relevant experience with Python and deep learning libraries such as Keras, Tensorflow and Pytorch
- A robust awareness of principles for carbon footprint analysis and reporting.
- A good contemporary understanding and interest in energy and environmental issues.
- Track record of publications in reputable journals would be an advantage.
- Have a strong written and oral command of the English language.
- Able to work independently and as part of a team.
- Curriculum vitae.
- A cover letter, of no more than 800 words, explaining the applicant's past research experience and aptitude in relation to the research project.