Drexel University · 21 hours ago
Postdoctoral Research Scientist - Machine Learning in Thin-Film Materials Science
Philadelphia, PA
Full-time
Onsite
New Grad, Entry Level
$55K/yr - $82K/yr
0+ years expDrexel University is seeking a Postdoctoral Research Scientist specializing in Machine Learning within the realm of Thin-Film Materials Science. The role involves conducting interdisciplinary research that combines materials science and AI/ML techniques to enhance materials discovery and analysis through data-driven approaches.
Higher Education
Responsibilities
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
Qualification
Required
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
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
Company
Drexel University
Drexel is an academically comprehensive and globally engaged urban R1 research university, known for the nation’s premier co-operative education program.
10001+ employees
H1B Sponsorship
Drexel University has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
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Trends of Total Sponsorships
2025 (31)
2024 (34)
2023 (36)
2022 (39)
2021 (32)
2020 (24)
Funding
Current Stage
Late StageLeadership Team
Larry Keiser, PhD
Co-Founder: Education, Learning and Brain Science (E-LaBS) Research Collaborative
Tamara Galoyan
Co-Director & Co-Founder, Education, Learning, and Brain Sciences (E-LaBS) Research Lab
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