Computational Material Scientist – DFT Expert

This is a fully remote position, open to applicants in Germany, +1 more country.

📋 Description

• Design, implement, and continually enhance DFT-based workflows to forecast the functional characteristics of crystalline materials.

• Create screening methodologies and decision-making criteria to effectively narrow down candidates from extensive search spaces.

• Facilitate the experimental validation of selected materials.

• Manage computational strategies for assigned discovery initiatives, encompassing milestones, success metrics, and deliverables.

• Propel the implementation of computational discovery programs.

• Develop robust, automated, and reproducible DFT pipelines.

• Enhance computational efficiency through AI/ML workflow optimization.

• Maintain comprehensive documentation, version-controlled workflows, and traceable datasets.

• Collaborate with synthesis and characterization teams to analyze results and resolve discrepancies.

• Integrate real-world experimental feedback into refined workflows and models.

• Present findings through written summaries and internal presentations.

• Contribute, when applicable, to publications, conference abstracts, and grant/partner materials while safeguarding IP strategy.

• Play a role in strategic decisions regarding methodology, prioritization, and platform direction.

⛳️ Requirements

• PhD in Physics, Chemistry, Materials Science, or a closely related discipline.

• Over 4 years of extensive hands-on DFT experience with crystalline materials.

• A strong record of publications and/or impactful open work.

• Proven experience in automating and accelerating DFT processes, including workflows, convergence optimization, error management, and high-throughput screening.

• Familiarity with applying AI/ML techniques for materials discovery and search.

• Proficient programming skills in Python or similar languages.

• Knowledge of version control, testing, and reproducibility practices.

• Ability to thrive in a dynamic startup environment that demands pragmatic prioritization, ownership, and collaboration.

• Experience with one or more of the following: VASP, Quantum ESPRESSO, GPAW, ABINIT, WIEN2k, CASTEP, or similar software.

• Understanding of phonons, elastic properties, defect calculations, magnetism/spin-orbit coupling, or dielectric/ferroelectric computations.

• Strong grasp of thermodynamics, phase stability, and materials databases such as Materials Project, OQMD, and ICSD.

• Preferred: Practical HPC experience with Slurm, job scheduling, profiling, and compute-cost optimization.

• Preferred: Familiarity with workflow management tools such as ASE, AiiDA, atomate, FireWorks, custodian, Prefect, Airflow, Snakemake, or Nextflow.

• Preferred: Experience in building or managing structured materials databases using Postgres, MongoDB, or parquet/lakehouse patterns.

• Preferred: Knowledge of materials representations including graph neural networks, CGCNN/MEGNet-like methods, descriptors, or symmetry-aware models.

• Preferred: Understanding of uncertainty quantification and decision-making under uncertainty for experimental planning.

• Preferred: Experience collaborating with experimental teams or in industrial R&D contexts.

• Preferred: A proven track record of translating predictions into validated materials and iterative learning cycles.

🏝️ Benefits

• Competitive compensation package.

• Potential for equity participation.

• Opportunities to publish selected results.

• Involvement in publications, conference abstracts, and grant/partner materials when appropriate.

• A small, highly interdisciplinary team environment.

• A culture that promotes ownership, curiosity, problem-solving, scientific breakthroughs, collaboration, and professional development.