Postdoctoral Research Position in Computational Physics at Harvard University

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Title Postdoctoral Research Position in Computational Physics at Harvard University

School Faculty of Arts and Sciences

Department/Area Physics

Postdoctoral Research Position in Computational Physics at Harvard University

The group of Professor Efthimios Kaxiras (Physics/ SEAS ) expects to appoint one postdoctoral fellow beginning in February 2026. We are looking for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application of methodologies suited for simulations of biological systems, including DNA and histone interactions.

Candidates should also arrange for letters of recommendation to be submitted by up to three scientists familiar with their work. Candidates may also wish to include selected preprints or reprints of articles they have written.

Candidate materials must include a CV and statement of research, in addition to the letters of recommendation.

Salary Range – Postdocs: $67,600-$80,000

Pay offered to the selected candidate is dependent on factors such as years of experience, training or qualification, field of scholarship, and accomplishments in the field for candidates with recent PhD (within 1 year of application).

Applications and supporting materials should arrive by 23 January 2026, for full consideration. However later applications may also be considered on a rolling basis.

Basic Qualifications

Candidates for this position must have completed their Ph.D. degree by the appointment start date and demonstrate outstanding research in computational physics for modeling of complex physical phenomena.

Additional Qualifications

Candidates must have experience in carrying out large-scale computational modeling of complex systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning approaches, and performing coarse-graining to obtain effective models at various scales.

Contact Email

Minimum Number of References Required 2

Maximum Number of References Allowed 3

Keywords

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