Postdoc - automated high-throughput screening of electrolyte adduces for aqueous batteries
Core
Develop automated high-throughput workflows for preparing and screening aqueous electrolyte additives to improve battery performance and stability.
Role type
Postdoctoral researcher (experimental + data analysis)
Builds
Reproducible, data-driven battery research workflows for stationary energy-storage technologies
Domain
Energy storage / Electrochemistry / Materials science
Deliverable
production ML models
Required skills
Electrochemistry, aqueous battery technologies, experimental laboratory work, electrochemical measurements, data analysis, machine learning, quantitative structure-property relationship modeling
Preferred skills
Aqueous batteries, metal-anode batteries, electrolyte development, materials characterization (microscopy, X-ray), automated experimentation, robotics, data science
Technologies
Galvanostatic cycling, cyclic voltammetry, electrochemical impedance spectroscopy (EIS), robotics, machine learning
Responsibilities
Develop and optimize automated high-throughput workflows; Systematically screen electrolyte additives and generate datasets; Perform electrochemical characterization; Analyze large experimental datasets and validate ML models; Publish results in peer-reviewed journals and present at conferences; Collaborate with researchers and industrial partners
Seniority
Postdoctoral researcher