Argonne National Laboratory is looking to solve the problem of developing and implementing experimental focused application of AI and automation tools for unraveling fundamental interfacial processes in materials for electrochemistry
Requirements
- Strong background in fundamental electrochemistry
- Hands-on expertise in computational materials science
- Code development skills
- Application of AI model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships
- Familiarity with optimization approaches such as genetic search, Bayesian optimization, etc
- Familiarity with electrochemical instrumentation suite (potentiostat, cyclers, electronic load) and materials characterization
Responsibilities
- Developing AI models to uncover structure-function relationships with limited data sets
- Building automated electrode-electrolyte interface discovery workflows
- Implementing full autonomous experimental campaigns
- Adapting workflows for automation and artificial intelligence optimization schemes
- Conducting computational as well as experimental laboratory work with a variety of electrochemical based methods
- Characterizing electrolytes using UV-VIs absorption spectroscopy, ICP-MS, LC-MS, GC-MS, and ICP-MS
- Learning experimental workflows and adapting them for autonomous control
Other
- Ph.D. completed in the past 5 years or soon-to-be completed in chemistry, chemical engineering, materials science, or a closely related field
- Good communication skills and fluency in both spoken and written English
- Ability to think and work independently and conduct research in a highly interdisciplinary environment
- Evidence of initiative and problem-solving in research projects
- CV/Resume with a brief description of research accomplishments, a list of publications, and the names of references
