Postdoktor inom AI-stödd mekanistisk förståelse
Core
Develop mechanistic understanding of coupled redox and dissolution mechanisms in multimetallic oxide systems using hydrometallurgical leaching of battery-related materials as a model system.
Role type
Postdoctoral researcher (experimental chemistry + data-driven modeling)
Builds
Predictive models linking process signals to reaction behavior for battery material recovery
Domain
Hydrometallurgy, battery recycling, redox chemistry, data-driven modeling
Deliverable
production ML models | research
Required skills
Experimental design, mechanistic modeling, time-resolved data analysis, statistical methods, machine learning, chemical kinetics, process monitoring
Preferred skills
Python, mechanistic/kinetic modeling of chemical processes, hydrometallurgical leaching, electrochemistry, real-time process monitoring, multicomponent chemical systems, ICP-OES/ICP-MS, XRD, SEM-EDS
Responsibilities
Conduct controlled leaching experiments in multimetallic oxide systems, analyze time-resolved measurements of pH, redox potential, temperature, and metal dissolution, investigate reaction kinetics and competing pathways, validate predictive models with independent experiments, communicate research results via publications and conference presentations, teach at undergraduate, master's, and doctoral levels
Seniority
Postdoctoral researcher, independent research with teaching duties