This position will be dedicated to research projects aiming developing and implementing experimental focused application of AI and automation tools for unraveling fundamental interfacial processes in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full autonomous experimental campaigns, this position is suited for a highly energetic and self-driven researcher willing to work in highly collaborative teams. This position will involve a considerable amount of computational as well as experimental laboratory work with a variety of electrochemical based methods (galvanostatic/potentiostatic, AC impedance, and hybrid potential/current control methods) coupled to analytical techniques for characterizing electrolytes using UV-VIs absorption spectroscopy, ICP-MS, LC-MS, GC-MS, and ICP-MS. This position will include learning experimental workflows and adapting them for autonomous control.
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Job Type
Full-time
Education Level
Ph.D. or professional degree
Number of Employees
1,001-5,000 employees