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Post-Doctoral Fellow

External
wpi logoWpi · Worcester
$45K–$70K/yrFull-timeOn-site2w ago
Data AnalysisLinuxMachine LearningPython
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Responsibilities

  • The postdoctoral fellow will:
  • Perform advanced first-principles calculations primarily using VASP
  • Conduct atomistic modeling of alloys, ceramics, and functional materials
  • Develop computational workflows for high-throughput simulations
  • Integrate machine learning methods with DFT-generated datasets
  • Develop and maintain scientific codes/scripts in Python or related languages
  • Analyze and interpret large-scale computational datasets
  • Publish research results in high-impact peer-reviewed journals
  • Assist in proposal preparation and collaborative research activities
  • Mentor graduate and undergraduate students as needed
  • Required Qualifications:
  • Applicants must have:
  • Ph.D. in Materials Science and Engineering, Physics, Chemistry, Mechanical Engineering, or a related field
  • Strong expertise in density functional theory (DFT)
  • Hands-on experience with VASP
  • Experience with Linux/HPC environments and parallel computing
  • Strong programming skills in Python or related scientific programming languages
  • Experience in scientific data analysis and workflow automation
  • Strong written and verbal communication skills

Requirements

  • Preference will be given to candidates with experience in one or more of the following areas:
  • Finite-temperature property calculations
  • CALPHAD or thermodynamic modeling
  • Defect calculations and diffusion modeling
  • Mechanical property calculations from first principles
  • Machine learning for materials science
  • Development of AI/ML workflows and surrogate models
  • High-throughput DFT calculations
  • Experience with ML interatomic potentials
  • Experience with materials databases and automated workflow frameworks
  • Experience with alloys, ceramics, oxides, or energy materials
  • Appointment Details:
  • Initial appointment is one year, renewable based on performance and funding availability.
  • Expected start date: July, 2026

Benefits

$45,000 - $70,000 per year. Salary is based on related experience, expertise, and internal equity. WPI's benefits package includes a robust retirement match, wellness perks, tuition assistance and more!To Apply:Applicants should submit:Curriculum VitaeBrief research statement describing relevant experience and interestsContact information for 2-3 referencesRepresentative publications (optional)For inquiries contact Prof. Yu Zhong, yzhong@wpi.edu , Department of Mechanical and Materials EngineeringApplications will be reviewed on a rolling basis until the position is filled.FLSA STATUSUnited States of America (Exempt)Vision insuranceEquity / stock options

Additional Information

JOB TITLE Post-Doctoral Fellow LOCATION Worcester DEPARTMENT NAME Mechanical & Materials Engineering - NFR JM DIVISION NAME Worcester Polytechnic Institute - WPI JOB DESCRIPTION SUMMARY The Integrated Materials and Processes Design (IMPD) group at Worcester Polytechnic Institute (WPI) invites applications for a Postdoctoral Fellow position in computational materials science with a focus on advanced density functional theory (DFT), machine learning, and multiscale materials modeling for alloys and ceramics. The successful candidate will conduct cutting-edge research involving first-principles calculations mainly using VASP and related computational tools to investigate elevated-temperature phase stability, thermodynamic and mechanical properties, defect chemistry, diffusion behavior, and electrical/functional properties of structural and energy materials. The position also involves the integration of machine learning and AI-assisted approaches into materials modeling workflows. The postdoctoral researcher is expected to work independently, develop computational workflows and machine-learning models, contribute to proposal and manuscript preparation, and collaborate with graduate students and external research partners. Potential research topics include: - Elevated-temperature phase stability of alloys and ceramics - Thermodynamic and kinetic modeling integrated with DFT - Mechanical property prediction at finite temperature - Defect chemistry and point defect energetics - Diffusion and transport properties - Electrical and functional property prediction - High-entropy materials and complex concentrated alloys - Oxides and energy materials - AI-assisted and machine learning-enhanced materials simulations - Development of automated computational workflows JOB DESCRIPTION


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