Postdoctoral Research Scientist - Machine Learning in Thin-Film Materials Science

Job Summary

The successful candidate will conduct interdisciplinary research at the intersection of materials science, thin-film materials, and artificial intelligence/machine learning (AI/ML). The position focuses on the synthesis, characterization, and understanding of advanced functional thin films, coupled with the development and application of data-driven and machine-learning approaches to accelerate materials insight, optimize growth processes, and extract structure-property relationships from complex experimental data. The researcher will work collaboratively in an experimental environment while contributing to the development of modern AI-enabled workflows for materials discovery and analysis.

Essential Functions

• Develop and apply AI and machine-learning methods to experimental materials data, including image-based, time-series, and multidimensional datasets arising from synthesis and characterization.
• Implement data workflows involving feature extraction, dimensionality reduction, pattern recognition, anomaly or change detection, and predictive modeling to identify growth regimes, structure-property trends, or emergent behaviors.
• Integrate physical understanding of materials with statistical and machine-learning models, emphasizing interpretability and relevance to materials physics.
• Design and execute thin-film deposition experiments using physical vapor growth techniques, with emphasis on multifunctional materials.
• Perform structural, electrical, and functional characterization of thin films, including X-ray diffraction, scanning probe microscopy, and related techniques to probe behavior at multiple length scales.
• Collaborate with experimentalists and theorists to connect data-driven insights.
• Prepare manuscripts, conference presentations, and reports documenting experimental and computational results.
• Mentor graduate and undergraduate researchers and contribute to a collaborative research environment.

Required Qualifications

• Minimum of a PhD or Doctorate in Materials Science and Engineering, Physics, Electrical Engineering, or a closely related field.
• Minimum of 0-3 years of experience.
• Working knowledge of AI and machine-learning techniques relevant to scientific data analysis, such as supervised and unsupervised learning, neural networks, or statistical learning methods.
• Demonstrated experience in thin-film synthesis and characterization.
• Experience analyzing complex experimental datasets using Python or similar scientific programming environments.
• Strong communication skills and ability to work across disciplinary boundaries.

Preferred Qualifications

• Experience applying machine learning to materials synthesis or characterization data.
• Background in scanning probe microscopy and nanoscale characterization.
• Familiarity with data-driven experiment design, automation, or real-time/near-real-time data analysis.
• Interest in developing generalizable, materials-agnostic AI workflows informed by physical insight.

Physical Demands

• Typically sitting at a desk/table
• Typically standing, walking
• Lifting demands 25lbs

Location

• University City - Philadelphia, PA

Additional Information
This position is classified as Exempt, grade K Compensation for this grade ranges from $54,630.00 - $81,940.00 per year. Please note that the offered rate for this position typically aligns with the minimum to midrange of this grade, but it can vary based on the successful candidate's qualifications and experience, department budget, and an internal equity review.

Applicants are encouraged to explore the Professional Staff salary structure and Compensation Guidelines & Policies for more details on Drexel's compensation framework. For information about benefits, please review Drexel's Benefits Brochure.

Special Instructions to the Applicant
Please make sure you upload your CV/resume and cover letter when submitting your application.

A review of applicants will begin once a suitable candidate pool is identified.

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