ARGONNE
Assistant Scientist – AI for Autonomous Synthesis and Multimodal Characterization
Lemont, IL USA · Contract
Sponsorship not specified$94k-$147kDetected 11 days ago
PythonMachine LearningDeep LearningTensorFlowPyTorchscikit-learnComputer VisionAgentic AIAI OrchestrationA/B TestingControlsResearchLeadershipCommunication
About the role
- This is an exciting opportunity to help shape a new generation of closed-loop, AI-enabled experimental workflows that tightly integrate synthesis within situ and operando x-ray, electron, and optical characterization.
- Please note that the pay range information is a general guideline only.
- Argonne encourages everyone to apply for employment.
Responsibilities
- Lead and develop a research program in AI-enabled autonomous materials synthesis
- Design and implement closed-loop experimental workflows that integrate synthesis, characterization, and decision-making
- Build analysis tools for multimodal, high-throughput experimental data, including real-time or near-real-time processing
- Collaborate closely with scientists across materials synthesis, characterization, beamline science, theory, and computing
Requirements
- Ph.D. in physical chemistry, inorganic chemistry, computational materials science, chemical engineering, or a related field, along with 3-6 years of postdoctoral research experience
- A strong understanding of nanomaterials synthesis and/or in situ/operando x-ray characterization (including scattering, spectroscopy, or imaging), with demonstrated experience connecting the two
- Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork
Nice to have
- Experimental control and orchestration frameworks such as ROS, Bluesky, or EPICS
- Laboratory automation and robotic synthesis platforms
- Multimodal data fusion and real-time data reduction for synchrotron or nanoscale experiments
- High-performance computing (HPC), edge-to-HPC workflows, and scientific data infrastructure
- Excellent written and verbal communication skills, with the ability to work effectively in a highly collaborative, multidisciplinary environment
- Application Materials
- Curriculum Vitae (CV)
- RD2: Bachelors and 5+ years of experience, Masters and 3+ years, or PhD and 0+ years, or equivalent
Compensation
- Please note that the pay range information is a general guideline only.
Benefits
- Develop and apply AI/ML methods for active learning, optimization, inverse design, and experiment planning
- Proven experience developing and applying AI/ML methods to autonomous experimentation, closed-loop optimization, active learning, or inverse design
- Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX
- Experience with optimization and active-learning libraries such as BoTorch, GPyTorch, or scikit-learn
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