Research Associate/Research Fellow

Research Associate/Research Fellow (Machine Learning and Mathematical Optimization) page is loaded## Research Associate/Research Fellow (Machine Learning and Mathematical Optimization)locations: NTU Main Campus, Singaporetime type: Full timeposted on: Posted Todayjob requisition id: R Established in 2010, the Energy Research Institute @ NTU ( ) is a pan-university research institute that focuses on systems-level research for tropical megacities. It performs translational research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. has multiple Interdisciplinary Research Programmes which focus on translational Research, Development & Deployment which focus on specific area of the energy value chain, and a number of Living labs and Testbeds which facilitate large scale technology deployment enabling validation and demonstration of real-world applications.For more details, please viewWe are looking for a Research Associate/Research Fellow to work on the project titled “Toward Cognitive Digital Twin as A Unified Platform for Smart Cities: Governance, Sustainability and Applications”, in collaboration with ITB, Bandung, Indonesia under the INSPIRASI program. The role will focus on developing machine learning and mathematical optimization solutions for electric vehicle fleet charging optimization under different constraints.**Key Responsibilities:*** Formulate and develop machine learning and mathematical optimization solutions for electric vehicle (EV) fleet charging scheduling problems with considerations of various smart power grids, battery degradation, and infrastructure level constraints.* Conduct and coordinate research within NTU and collaborating partners in line with its research strategy.* Publish research outcomes in journals of international standing; Attend and present research findings and papers at academic meetings.* Collaborate closely with our partner ITB to apply solutions in a local context, using data available with ITB.* Assist ITB in the development of their digital twin platform by integrating the solutions developed in NTU into their platform.**Job Requirements:*** Masters in Computer Science, Electrical Engineering or related field (for Research Associate)* PhD in smart grids, energy management, or mathematical optimizations (for Research Fellow). The candidates who have submitted the thesis are also encouraged to apply.* Background in the application of machine learning and mathematical optimization techniques, including reinforcement learning* Proven publication track record in similar domains for Research Fellow position* Proficiency in written and spoken English for research work.* Interpersonal skill (e.g. Ability to work independently / develop solutions under strict timelines, meticulous* and eye for details / excellent organizational / time management skills)* Desirable: Background in the modeling of energy management systems (e.g., buildings, batteries, smart grids, vehicle-to-grid, etc.).We regret to inform that only shortlisted candidates will be notified.Hiring Institution: NTUNTU is also home to world-class autonomous institutes – the National Institute of Education, S Rajaratnam School of International Studies, Earth Observatory of Singapore, and Singapore Centre for Environmental Life Sciences Engineering – and various leading research centres such as the Nanyang Environment & Water Research Institute (NEWRI) and Energy Research Institute @ NTU ( ).Ranked amongst the world’s top universities by QS, NTU has also been named the world’s top young university for the past seven years. The University’s main campus is frequently listed among the Top 15 most beautiful university campuses in the world and has 57 Green Mark-certified (equivalent to LEED-certified) buildings, of which 95% are certified Green Mark Platinum. Apart from its main campus, NTU also has a campus in Novena, Singapore’s healthcare district.Under the NTU Smart Campus vision, the University harnesses the power of digital technology and tech-enabled solutions to support better learning and living experiences, the discovery of new knowledge, and the sustainability of resources.For more information, visit #J-18808-Ljbffr