Posting Expiry
Postdoctoral Position
The Scalable Optimization, Learning, and Intelligent Decision-making (SOLID) Lab at the University of British Columbia (UBC) is seeking a highly motivated Postdoctoral Research Fellow to work on a funded research project on AI-enabled innovation for vanadium flow batteries (VFBs).
The project aims to develop advanced AI and machine-learning methods for predicting battery degradation and accelerating the discovery of next-generation electrolyte and membrane materials. The research will leverage large-scale operational battery data together with laboratory and simulated datasets.
The postdoctoral fellow will contribute to research in areas including:
- Machine learning and data analytics for battery degradation prediction;
- Physics-informed and hybrid machine-learning models;
- Time-series modelling using deep learning and transformer architectures;
- Explainable AI for identifying factors affecting battery degradation and lifetime;
- Generative AI, active learning, and multi-objective optimization for battery materials discovery;
- Integration of field, simulation, and experimental data for model development and validation.
The project provides an opportunity to work with large-scale industrial battery datasets containing operational variables such as current, voltage, temperature, and flow rate. The developed models will be used to understand degradation behaviour and support the optimization of battery operating strategies. The project will also explore active learning and generative AI to identify promising electrolyte and membrane materials and reduce the number of experiments required for materials development.
About the Lab
The Scalable Optimization, Learning, and Intelligent Decision-making (SOLID) Lab, led by Prof. Yankai Cao in the Department of Chemical and Biological Engineering at UBC, develops advanced machine-learning and optimization methods for complex engineering systems.
Our research lies at the intersection of artificial intelligence, process systems engineering, optimization, and energy systems, with particular interests in scalable optimization, interpretable and physics-informed machine learning, data-driven modelling and control, and AI-enabled energy technologies.
The successful candidate will join an interdisciplinary research environment and have opportunities to work on fundamental AI/ML methodology development as well as real-world energy applications.
Qualifications
Applicants should have, or be close to completing, a PhD in Chemical Engineering, Electrical Engineering, Mechanical Engineering, Computer Science, Applied Mathematics, Materials Engineering, or a closely related field.
Strong candidates will have experience in one or more of the following areas:
- Machine learning and deep learning;
- Time-series modelling;
- Physics-informed or hybrid modelling;
- Optimization and active learning;
- Battery modelling and battery degradation;
- Computational materials discovery;
- Large-scale industrial data analytics.
Strong programming skills, particularly in Python, are expected. Experience with PyTorch, TensorFlow, scientific computing, or large-scale data processing would be an asset.
Candidates should have a strong publication record, good written and oral communication skills, and the ability to conduct independent research and work effectively in an interdisciplinary environment.
Prior experience in batteries or electrochemistry is desirable.
How to Apply
Interested candidates should email Prof. Yankai Cao at yankai.cao@ubc.ca with the subject line “Postdoctoral Application – AI for Vanadium Flow Batteries.”
Please include:
- A CV;
- A brief cover letter describing your research background and interest in the position; and
- Contact information for 2–3 references.
Applications will be reviewed as they are received until the position is filled. The anticipated start date is April 1, 2027.
Salary Ranges
The salary for this position is CAD $70,000 per year plus approximately 20% in benefits.
Candidates who hold external postdoctoral awards or fellowships (e.g., an NSERC Postdoctoral Fellowship) will receive an additional top-up.