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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in computational chemistry and materials science, with a strong focus on DFT, quantum mechanics, and machine learning applications in atomistic modeling. Proven ability to lead projects, mentor team members, and engage with both academic and industrial partners.
Highest-signal resume keywords
PhD In Computational ChemistryDeep Expertise In Quantum MechanicsExtensive Experience With VASPStrong Experience With ML ModelsProficiency In Python
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
DFTThermodynamic ModelingKinetic ModelingAtomistic ModelingLarge-Scale DFT CampaignsMLIPsFine-Tuning WorkflowsTransfer LearningActive Learning WorkflowsReaction Network Generation
Soft Skills
Strong Analytical SkillsExcellent Communication SkillsAbility To Drive Projects Independently
Tools & Technologies
VASPCP2KOrcaLAMMPSAtomateJobflowFireworksASEPyTorchSlurm
Industry Keywords
SemiconductorsBatteriesElectrocatalysisCarbon-Intensive IndustriesAtomic-Scale Manufacturing
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Lead investigations into surface reaction mechanistic modeling
- Perform thermodynamic and kinetic modeling of solvated interfaces using DFT and MLIPs
- Drive automation and orchestration of high-throughput simulation pipelines within the active learning framework
- Reduce human-in-the-loop bottlenecks and enable faster iteration cycles
- Contribute to publications, patents, and client-facing deliverables
- Mentor junior team members and interns
- Engage with industrial and academic partners
- Ensure computational discoveries are experimentally grounded
- Advance atomistic modelling capabilities across R&D challenges in atomic-scale manufacturing
- Work on applications relevant to semiconductors, batteries, electrocatalysis, and other carbon-intensive industries
Requirements
What you’ll need- PhD in Computational Chemistry, Materials Science, Chemical Physics, or a closely related field
- 2+ years of industry experience
- Deep expertise in quantum mechanics and DFT
- Extensive hands-on experience with VASP, CP2K, Orca, LAMMPS, or equivalent
- Strong understanding of underlying physics
- Proven experience designing, running, and maintaining large-scale DFT campaigns
- Experience with workflow managers such as Atomate, Jobflow, Fireworks, or ASE workflows
- Strong experience with ML models applied to atomistic systems
- Knowledge of MLIPs or molecular property prediction models
- Experience with fine-tuning, transfer learning, or active learning workflows is a strong asset
- Familiarity with at least one of kMC, QM/MM, or related mesoscale methods is a significant plus
- Proficiency in Python, PyTorch, Slurm, and Git
- Strong analytical skills and scientific rigour
- Ability to drive projects independently in a fast-paced startup environment
- Excellent communication skills in English
- Ability to present complex results to technical and non-technical audiences
- Bonus: experience modeling phase behavior and material properties of alloys
- Bonus: experience modeling electronic transport at surfaces, including solvated surfaces
- Bonus: familiarity with reaction network generation or automated transition state search tools
Benefits
Comp & perks- Equity (BSPCE)
- Comprehensive health insurance (Alan blue)
- French level paid leave and time-off work
- Dynamic work environment with strong preference for in-person collaboration at Paris offices and flexibility for occasional remote work
- Relocation package
- Thorough visa support
- Access to conferences
- Internal learning sessions
- Compute resources