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Postdoctoral Associate
  • Job Number: 25605
  • Functional Area: Research - Engineering
  • Department: Mechanical Engineering
  • School Area: Engineering
  • Pay Range Minimum: $71,000
  • Pay Range Maximum: $90,000
  • Employment Type: Full-Time
  • Employment Category: Exempt
  • Visa Sponsorship Available: Yes
  • Schedule:
  • Pay Grade: No Grade


Posting Description

POSTDOCTORAL ASSOCIATE, Mechanical Engineering - The Accelerated Materials Lab for Sustainability (AMLS), to lead the development of perovskite photovoltaic devices as part of a fully autonomous materials discovery platform. Our lab operates at the intersection of materials science, robotics, and physical AI to accelerate green energy research. The autonomous platform screens thousands of perovskite compositions per day and identifies promising candidate absorbers for further study. This postdoctoral position will lead the translation of those candidate materials into high-quality thin films and photovoltaic devices; execute a staged fabrication pipeline spanning candidate selection, thin-film deposition, high-throughput device fabrication, and high-performance device fabrication; and be central to demonstrating that autonomous materials discovery can produce real improvements in photovoltaic performance and stability.

The full job description is available, here: https://drive.google.com/file/d/17SY2HfckkPZp8GVtRelkIxrGhikP92nN/view?usp=sharing

Job Requirements

REQUIRED: PhD in materials science, physics, electrical engineering, chemical engineering, or a related field by the start date; demonstrated hands-on experience fabricating, characterizing, and optimizing perovskite photovoltaic films and devices; experience with thin-film processing methods such as spin coating and with key photovoltaic characterization techniques such as J-V measurement, maximum power point tracking, and X-ray diffraction; strong experimental troubleshooting, analytical, and scientific writing skills; ability to conduct independent research and work effectively in a collaborative laboratory environment; and excellent communication skills and ability to mentor undergraduate researchers. PREFERRED: Experience with novel or high-throughput characterization methods; accelerated stress testing and stability evaluation of thin-film photovoltaics; and data-driven experimental optimization, including Bayesian optimization or machine learning methods relevant to materials research.

4/2/2026