Computational Condensed Matter Physicist at lilasciences

Compensation

$176,000 – $304,000/yr

Setup: Remote
Location: Cambridge, Minnesota
Type: Full-time
Level: not_specified
Posted: Jul 28, 2026

Description

LILA Sciences is seeking a Computational Condensed Matter Physicist to drive materials discovery and optimization using computational condensed matter physics, electronic structure expertise, and AI/ML techniques. This role involves developing predictive models, analyzing simulation/experimental data, and collaborating with interdisciplinary teams to advance scientific workflows.

Required Skills

Computational Condensed Matter Physics
Electronic Structure Expertise
First-principles Modeling
Atomistic Simulations
Scientific Machine Learning
Agentic AI Systems
Superconductors
Quantum Materials
Electronic Devices
Python

Additional Information

Minimum Education: PhD or equivalent experience in Physics, Materials Science, Chemistry, Applied Mathematics, or a related field.
Required Previous Experiences: Strong foundation in computational condensed matter physics, electronic structure, or atomistic simulation.
Deep understanding of electronic-structure theory (quantum chemistry, DFT, beyond-DFT methods, etc.) and their application to electronic, magnetic, or quantum materials.
Experience applying first-principles or atomistic methods to materials discovery, optimization, or understanding.
Preferred Qualifications: Experience working with amorphous materials, vibrational properties calculations and advanced electronic structure methods.
Experience applying AI/ML to computational materials science or physics-based simulation data.
Familiarity with agentic AI systems, autonomous scientific workflows, or simulation-aware agents.
Background working with quantum materials, superconductors, semiconductors, or electronic device materials.
Experience integrating computational predictions with experimental characterization, device measurements, or closed-loop optimization workflows.
Ability to communicate physical insight, uncertainty, and model limitations to cross-functional collaborators.