Postdoktor inom fastfas-batterier: ML-baserad batteriestimering & reglering
Core
Developing reduced-order models, state estimation methods, and predictive control strategies for solid-state batteries using physics-based modeling combined with machine learning.
Role type
Postdoctoral researcher (control systems & machine learning)
Builds
Physics-informed ML models for battery state estimation and control
Domain
Energy storage / Solid-state batteries / Control theory
Deliverable
production ML models
Required skills
state estimation, dynamic system modeling, control and optimization, machine learning, physics-based modeling, X-ray CT data analysis, quantitative microstructure analysis
Preferred skills
solid-state battery experience, electrochemical system modeling
Technologies
X-ray CT, electrochemical measurement systems
Responsibilities
Develop reduced-order models for coupled electrochemical and mechanical processes; implement state estimation and predictive control strategies; analyze electrochemical measurement data and structural characterization data; reconstruct and segment X-ray CT data; quantify microstructure analysis.
Seniority
Postdoctoral, research-focused IC