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Postdoctoral Fellow in Material Science & Mechanical Engineering

Harvard University

Harvard University

Other Engineering
Posted on Mar 13, 2026

Position

Details
Title Postdoctoral Fellow in Material Science & Mechanical Engineering
School Harvard John A. Paulson School of Engineering and Applied Sciences
Department/Area Material Science & Mechanical Engineering
Position Description
The Materials Intelligence Research group of Prof. Boris Kozinsky at Harvard University is seeking researchers at the postdoc level to develop and apply first principles and machine learning methods for computational materials physics and chemistry.
Projects include:
  1. The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned interatomic potential to incorporate materials response. Resulting models are implemented in LAMMPS and are used to perform ML-accelerated large-scale dynamics simulations to investigate the evolution of polarization and magnetization degrees of freedom. Efforts are also aimed at learning computationally lean and geometrically rich representations and designing methods for quantifying uncertainty of predictions. Qualifications: Familiarity with machine learning interatomic potentials, CPU and GPU parallelization, knowledge of LAMMPS and molecular dynamics, experience with first principles calculations of dielectric and magnetic response.
  1. Development of machine learning methods for exchange-correlation functionals. Current work in the group is focused on improvements and performance optimizations for the recently developed CIDER formalism for designing non-local XC functionals, with an eye toward applying the resulting functionals to currently intractable problems in catalysis and energy storage materials. Effort is aimed at generating training sets with high-order quantum calculations and designing combined models for the exchange and correlation energy using non-local features of electronic density and orbitals. Qualifications: Experience with developing efficient numerical algorithms and modifying electronic structure DFT or quantum chemistry software (e.g. Quantum Espresso, PySCF, GPAW), fluency with electronic structure theory and quantum chemistry, experience with machine learning regression methods.
Preferred start date as soon as possible but flexible.
Basic Qualifications
PhD in Physics, Chemistry, Materials Science, Computer Science, Applied Mathematics or related fields by the time the appointment begins.
Additional Qualifications
Special Instructions
Applicants should include a full CV, cover letter summarizing your experience, list of reference contacts (minimum of 3), and up to 3 publications.



Contact Information
Prof. Boris Kozinsky
Contact Email bkoz@g.harvard.edu
Salary Range
$67,600 – $91,826
Pay offered to the selected candidate is dependent on factors such as rank, years of experience, training or qualification, field of scholarship, and accomplishments in the field.
Minimum Number of References Required 3
Maximum Number of References Allowed 3
Keywords

EEO/Non-Discrimination Commitment Statement

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes.

Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non-discrimination policy. Harvard’s equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.

Supplemental Questions

Required fields are indicated with an asterisk (*).

Applicant Documents

Required Documents
  1. Curriculum Vitae
  2. Cover Letter
Optional Documents
  1. Publication
  2. Publication 2
  3. Publication 3