Postdoctoral Researcher - Autonomous Experimentation for Semiconductor Materials

A postdoctoral research position is available in the Materials Discovery and Development group, focused on autonomous experimentation at the intersection of semiconductor materials research, artificial intelligence methods, and research equipment automation.

Our team is developing a next-generation autonomous semiconductor research laboratory as part of the U.S. Department of Energy's METALLIC platform. The successful candidate will support building closed-loop experimental systems that integrate advanced scientific instrumentation, AI-driven decision-making, data infrastructure, and workflow orchestration. The platform will accelerate the discovery of advanced thin film inorganic semiconductor materials that address critical U.S. supply chain challenges, with particular emphasis on semiconductor technologies involving Ga, Ge, Sc and other critical elements and materials. The successful candidate will also have opportunities to contribute to broader autonomous experimentation initiatives across NLR, including the U.S. Department of Energy's Genesis Mission projects aimed to accelerate scientific discovery in wurtzite oxide and nitride wide band gap semiconductor, as well as dielectric, piezoelectric, and ferroelectric materials.

As a part of these projects, the successful candidate will develop and apply autonomous experimentation approaches across multiple synthesis and characterization instruments, including vacuum deposition systems such as sputtering, pulsed laser deposition, and molecular beam epitaxy (MBE), as well as advanced property characterization tools, such as photoluminescence, profilometry, J-V, C-V, P-E and other electrical measurements, that complement combinatorial structural (XRD) and compositional (XRF) data. This position offers an opportunity to develop and deploy new approaches in AI-driven experimentation, scientific software, robotics, and laboratory automation across a large and rapidly evolving set of experimental instruments.

As part of this position, the successful candidate will:

• Design and deploy advanced algorithms and closed-loop workflows for autonomous experimentation on synthesis and characterization instruments.
• Integrate laboratory instrumentation, data infrastructure, and workflow orchestration systems into scalable autonomous experimentation platforms.
• Deploy the developed computer vision, robotics, and artificial intelligence methods to solve challenging materials science and semiconductor research problems.
• Collaborate with materials scientists, engineers, and research technologists to apply autonomous experimentation approaches to other research projects.
• Proactively diagnose and troubleshoot scientific instrumentation, automation hardware, and software interfaces in accordance with safety practices and operational procedures.

Note: Applicants should submit a cover letter (along with their CV) describing how their background and experience align with the required qualifications for this position, and any preferred qualifications they have in the areas of semiconductor materials research, autonomous experimentation methods, specific software and hardware tools listed above. The cover letter should also include links to relevant public GitHub repositories or other accessible examples of prior software development work (e.g. journal publications, conference proceedings) and briefly describe the applicant’s individual contributions to those projects. We encourage anyone who is interested in this opportunity to apply, even if they don't meet 100% of the position requirements.

Basic Qualifications

Must be a recent PhD graduate within the last three years.

* Must meet educational requirements prior to employment start date.

Additional Required Qualifications

• Demonstrated hands-on proficiency in Python, including the ability to independently design, implement, understand, debug, and maintain scientific software without reliance on AI-assisted coding tools.
• Proven knowledge of modern software development practices, including Git-based version control, modular software design, testing, and documentation, is required
• Prior experience in developing software for scientific instrumentation, laboratory automation, experimental control, or related hardware applications, and its integration with scientific objectives into robust experimental workflows.
• Experience with machine learning, statistical modeling, optimization algorithms, or data-driven scientific methods, such as active learning, Bayesian optimization, design of experiments, uncertainty quantification, or related methods.
• Basic knowledge of materials science, inorganic chemistry, semiconductor physics, vacuum based thin film processing and characterization methods, composition/structure/property relations, and experimental laboratory equipment

Preferred Qualifications

Preferred semiconductor materials research qualifications:

• Prior hands-on experience developing autonomous or closed-loop experimental laboratory instruments, including high-throughput and combinatorial experiments
• Experience with vacuum-based thin-film synthesis and characterization equipment, such as PVD, PLD or MBE  systems, electrical probe stations, photoluminescence instruments, optical microscopes, or related scientific instrumentation.
• Knowledge of inorganic thin films and semiconductor materials, particularly oxides, nitrides, wide band gap semiconductors and dielectrics, or related material systems.

Preferred autonomous experimentation qualifications:

• Experience with databases, scientific data pipelines, or experimental data management. Experience deploying scientific software across networked Linux and/or Windows systems in a plus.
• Experience implementing feedback control systems, state machines, fault handling, or other control logic for automated experimental systems. Experience with robotics, motion control, machine vision, or robotic system integration in a plus.

Preferred specific software and hardware qualifications:

• Experience integrating and controlling scientific instrumentation through software APIs, instrument command sets (e.g., SCPI), industrial communication protocols (e.g., Modbus RTU/TCP), and interoperability standards (e.g., OPC UA).
• Experience with distributed systems and messaging architectures (e.g., NATS, MQTT, ZeroMQ, or similar publish/subscribe and message-oriented systems).
• Experience with PLC programming using IEC 61131-3 languages, particularly Structured Text (ST) and Ladder Diagram (LD); experience with Red Lion Graphite Edge Controllers and Crimson software is a plus.