Established in 2010, the Energy Research Institute @ NTU (ERI@N) 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. ERI@N 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 view https://www.ntu.edu.sg/erian
We are looking for a Research Assistant to support an AI-enabled battery recycling research project that advances sustainable energy and circular economy goals. The role will focus on developing data-driven models and decision tools to improve the efficiency, yield, and cost effectiveness of battery recycling workflows, supporting experimental and simulation studies across the battery lifecycle, and implementing algorithms for process monitoring and optimization so that recycling operations can be more reliable, scalable, and environmentally responsible.
Key Responsibilities:
Conduct literature review and problem scoping on battery recycling, including process routes such as hydrometallurgy, pyrometallurgy, and direct recycling.
Build datasets from experiments, sensors, and public sources, and then perform data cleaning, labeling, and feature engineering for battery recycling tasks.
Develop AI and machine learning models for recycling process prediction and decision support, such as forecasting metal recovery, impurity levels, energy use, and emissions.
Develop optimization and control methods for recycling resource management, such as reagent usage, temperature profiles, scheduling, and throughput, under safety and quality constraints.
Support experimental coordination and analysis, including interpretation of characterization and assay results (as applicable), and then translate findings into model inputs and evaluation.
Prepare technical reports, documentation, and research manuscripts, and also contribute to project presentations and meeting updates.
Collaborate with the PI and team members, and coordinate with external partners when needed, to ensure milestones and deliverables are met.
Job Requirements:
Bachelor qualification degree in Computer Science, Electrical Engineering, or related disciplines
Strong research experience with a good track record of publications.
Familiarity with machine learning and optimization, such as supervised learning, reinforcement learning, or constrained optimization.
Good written and oral communication skills for preparing technical reports, presentations, and draft manuscripts.
Proficiency in hard skills, such as implementing and evaluating algorithms in Python, and maintaining reproducible experimentation.
Competent in soft skills, such as problem scoping and analytical skills for experimental results interpretation.
Interpersonal skill, such as good teamwork and collaboration skills to work closely with the PI, team members, and external partners.
Entry level candidates are welcome to apply.
We regret to inform that only shortlisted candidates will be notified.
