Research Scientist, Computational Condensed Matter Physics

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA · Physical Sciences AI

Research Scientist role applying computational physics and AI/ML to materials discovery, focusing on agentic AI systems for scientific workflows and reasoning over simulation/experimental data. Requires PhD in a scientific field, strong programming in Python, and expertise in computational condensed matter physics or electronic structure. Bonus for AI/ML experience in materials science and familiarity with agentic AI systems.

What you'd actually do

  1. Apply computational condensed matter physics to materials discovery and optimization.
  2. Use computational physics methods (such as electronic structure and phonon calculations) to study quantum materials, superconductors and electronic devices.
  3. Connect simulation outputs to experimental observations and develop workflows that close the loop between computation and experiment.
  4. Build predictive models from computational and experimental data to guide materials selection and optimization.
  5. Analyze simulation and experimental data to generate actionable materials hypotheses.

Skills

Required

  • PhD or equivalent experience in Physics, Materials Science, Chemistry, Applied Mathematics, or a related field
  • 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
  • Familiarity with superconductors, quantum materials, electronic materials, semiconductors, or device-relevant materials systems
  • Strong programming skills in Python and scientific computing workflows

Nice to have

  • 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.

What the JD emphasized

  • PhD or equivalent experience in Physics, Materials Science, Chemistry, Applied Mathematics, or a related field.
  • 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.
  • Familiarity with superconductors, quantum materials, electronic materials, semiconductors, or device-relevant materials systems.
  • Strong programming skills in Python and scientific computing workflows.

Other signals

  • applying computational condensed matter physics and electronic structure expertise to accelerate materials discovery, optimization, and understanding
  • using first-principles modeling, atomistic simulations, scientific machine learning, and agentic AI systems to investigate complex materials
  • work at the intersection of physics-based simulation, AI/ML, and autonomous scientific workflows
  • using computational insight to identify promising materials, explain structure-property relationships, guide optimization, and help agents reason over simulation and experimental data in scientifically grounded ways