MSE Tenure-Track Positions 2026–2027

University of Pennsylvania; Philadelphia (Philadelphia County); On-site; USD 120,000 - 180,000; Full time

Job summary

The Department of Materials Science and Engineering at the University of Pennsylvania is inviting applications for tenure-track faculty positions for 2026–2027, located in Philadelphia. We welcome experimental and computational research focusing on AI/ML, data science, and materials computation to advance electronic, structural, and energy-related materials.

We seek candidates who will build a collegial scholarly community, train students, and contribute to Penn's signature initiatives in data

Qualifications

• Ph.D. in Materials Science and Engineering or related disciplines.
• Strong background in AI/ML, data science, and materials computation.
• Ability to collaborate across disciplines and mentor students.

Responsibilities

• Conduct research in materials discovery, synthesis, and characterization.
• Develop and apply AI/ML tools to accelerate materials design and processing.
• Collaborate across Penn Engineering and university-wide initiatives; supervise students and junior researchers.

Skills

AI/ML experience

Data science

Materials computation

Interdisciplinary collaboration

Mentoring students

Education

Ph.D. in Materials Science and Engineering or related disciplines

Job description

MSE Tenure-Track Positions 2026-2027

Location: Philadelphia, PA

Open Date: Oct 13, 2026

Deadline:

The Department of Materials Science and Engineering at the University of Pennsylvania is conducting a multi-year search for tenure-track faculty whose research aligns with the strategic plan of the School of Engineering and Applied Science (Penn Engineering). We invite applications from all experimental and computational areas of materials research. We have particular interest in candidates who develop or apply emerging methods in artificial intelligence (AI), machine learning (ML), data science, and materials computation. We are interested in these methods as tools to advance the discovery, synthesis, characterization, and production of electronic materials, structural materials, and materials that address challenges in energy and environmental sustainability. Penn has signature initiatives in data science, energy and sustainability, and quantum devices and engineering (QUIEST). Penn also has shared facilities at the Singh Center for Nanotechnology, the Vagelos Laboratory for Energy Science and Technology (VLEST), and the Laboratory for Research on the Structure of Matter (LRSM).

We seek colleagues who will help build a collegial scholarly community within the department, across Penn Engineering, and throughout the University. We also seek colleagues who will work closely with students to identify and address research challenges.

Penn Engineering strongly supports dual-career couples. We encourage early inquiries about dual-career assistance for both academic and non-academic opportunities.

Materials Discovery, Synthesis, and Characterization for Advanced Applications

Progress in materials science and advanced manufacturing depends on the discovery, development, and characterization of new electronic, structural, sustainable, and energy-related materials systems. The next generation of highly engineered materials is expected to be synthesized with control over several features at once. These features are local atomic bonding and structure, anisotropy, the ordering of defects, charges, and spins, and dimensionality and topology. We seek candidates with experimental or computational expertise who develop modern tools that integrate AI, ML, data science, and materials computation to accelerate materials discovery, synthesis, characterization, and processing. Of particular interest are AI/ML and computational approaches to materials prediction and design, autonomous experimentation, synthesis and processing, and characterization. This includes methods that combine physical models, simulation, data, and experiment.

Areas of interest include, but are not limited to:

• Autonomous and AI-driven materials discovery, characterization, optimization, synthesis, and processing
• Data-driven design and synthesis of solid-state materials
• Scalable methods for producing and integrating materials across all length scales
• Prediction and control of material composition, structure, and properties at multiple length scales
• Translation of fundamental materials science into electronics, photonics, sensing, and energy applications

The ideal candidate will demonstrate the ability to predict, control, and interrogate material composition, structure, and properties at multiple length scales to enable next-generation devices and systems. Candidates should have a strong background in materials theory, computation, synthesis, or characterization. They should also have a clear vision for how their approach will improve device performance and functionality and accelerate materials innovation. We particularly encourage applications from candidates who connect fundamental materials science to device and system performance and who are committed to fostering interdisciplinary collaboration.

Prof. Christopher B. Murray chairs the search committee. Review of applications will proceed on a rolling basis and candidates are encouraged to submit materials early for full consideration. All application materials must be received by December 1, 2026.

Qualifications

Must have a Ph.D. in Materials Science and Engineering or related disciplines.

Application Instructions

Applications must be submitted online via Interfolio. Applications include:

• Cover Letter
• Curriculum vitae
• Research statement (5-page limit)
• Teaching statement (2-page limit)
• Three confidential letters of recommendation

Equal Employment Opportunity Statement

The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.