Machine Learning Quantum Computing Jobs in Chicago, IL

TheCenter for Nanoscale Materials (CNM)and theAdvanced Photon Source (APS)atArgonne National Laboratoryinvite applications for ajoint Assistant Scientistposition focused on developing and applyingartificial intelligence (AI)andmachine learning (ML)methods for the autonomous, self-driving synthesis ofnanoscale and quantum materials.

This is an exciting opportunity to help shape a new generation ofclosed-loop, AI-enabled experimental workflowsthat tightly integrate synthesis within situ and operando x-ray, electron, and optical characterization. The successful candidate will help bridge CNM's world-class capabilities innanofabrication and chemical synthesiswith APS's leadingsynchrotron measurement tools, enabling adaptive and autonomous exploration of complex materials design spaces.

In this role, you will lead a research program centered onAI-driven autonomous synthesis, including:

• Active learning and Bayesian optimization over synthesis parameters such asprecursors, temperature, sequences, and pressure

• Generative and inverse-design models formaterials discovery

• Closed-loop feedback frameworks that usein situ/operando scattering, spectroscopy, and imagingto guide synthesis in real time

• AI-enabled analysis ofhigh-throughput, multimodal experimental datawithuncertainty quantification

• Integration ofedge computing, high-performance computing (HPC), and scientific data infrastructureto support scalable, user-facing autonomous workflows across CNM synthesis platforms and APS beamlines

This position is ajoint appointmentbetween theTheory and Modeling Group at CNMand theComputational Science and AI Group (CAI) at APS. The successful candidate will have access to Argonne's exceptional ecosystem of facilities and expertise, including the upgraded APS, CNM's advanced synthesis and characterization capabilities, and leadership-class computing resources at theArgonne Leadership Computing Facility.

Key Responsibilities

• Lead and develop a research program inAI-enabled autonomous materials synthesis

• Design and implementclosed-loop experimental workflowsthat integrate synthesis, characterization, and decision-making

• Develop and applyAI/ML methodsfor active learning, optimization, inverse design, and experiment planning

• Build analysis tools formultimodal, high-throughput experimental data, including real-time or near-real-time processing

• Collaborate closely with scientists acrossmaterials synthesis, characterization, beamline science, theory, and computing

• Contribute to the development of scalable computational and data workflows spanningedge, beamline, and HPC environments

• Publish in peer-reviewed journals, present at scientific meetings, and help shape future directions in autonomous materials research

Position Requirements

• Ph.D.inphysical chemistry, inorganic chemistry, computational materials science, chemical engineering, or a related field, along with3-6 years of postdoctoral research experience

• A strong understanding ofnanomaterials synthesisand/orin situ/operando x-ray characterization(including scattering, spectroscopy, or imaging), with demonstrated experience connecting the two

• Proven experience developing and applyingAI/ML methodstoautonomous experimentation, closed-loop optimization, active learning, or inverse design

• A strong publication record demonstrating innovation inAI/ML for materials synthesis, synchrotron experiments, or a closely related area

• Experience with deep learning frameworks such asPyTorch, TensorFlow, or JAX

• Experience with optimization and active-learning libraries such asBoTorch, GPyTorch, or scikit-learn

• Strong programming skills, especially inPython, including integration withexperimental control systems or lab-automation frameworks

• Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork

Preferred Qualifications

• Experimental control and orchestration frameworks such asROS, Bluesky, or EPICS

• Laboratory automation androbotic synthesis platforms

• Generative models, reinforcement learning, or agentic AI approachesfor materials discovery and experiment planning

• Multimodal data fusionand real-time data reduction for synchrotron or nanoscale experiments

• High-performance computing (HPC), edge-to-HPC workflows, and scientific data infrastructure

• Digital twins, physics-informed machine learning, or simulation-augmented experiment design

• Excellent written and verbal communication skills, with the ability to work effectively in ahighly collaborative, multidisciplinary environment

Application Materials

Please upload the following as part of your application:

• Curriculum Vitae (CV)

• Cover Letter

RD2: Bachelors and 5+ years of experience, Masters and 3+ years, or PhD and 0+ years, or equivalent

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Please note that the pay range information is a general guideline only. 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. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.