Postdoctoral Appointee - Humanoid and dexterous robotics development
About the role
The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be the development of AI models for robotic control and the demonstration of these methods via simulation and experiment. Beyond the listed projects, the candidate may contribute to other large-team scientific projects in artificial intelligence, materials engineering, chemistry, and beyond at Argonne National Laboratory.
Responsibilities
- Develop AI models for robotic control
- Demonstrate AI methods via simulation and experiment
- Contribute to other large-team scientific projects in artificial intelligence, materials engineering, chemistry, and beyond
Requirements
- Recently completed PhD within the last 0-5 years in computer science, mechanical engineering, artificial intelligence, or related engineering disciplines
- Experience with state of the art AI methods for robotics
- Experience in deep learning including data collection, architecture development, model training, and validation
- Interest in software development, with particular emphasis on the Python programming language and contributions to open-source scientific software
- Scientific productivity, as demonstrated by publications and conference presentations
- Effective oral and written communication skills
Qualifications
- Ability to model Argonne's core values of impact, safety, integrity, respect, and teamwork
Skills
- Python programming
- Contributions to open-source scientific software
Benefits
Comprehensive benefits are part of the total rewards package. Click here to view Argonne employee benefits!
Pay
The expected hiring range for this position is $72,879.00-$121,465.00. Please note that the pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs.