Computational Material Scientist – DFT Expert

🔥 0 minutes ago

🌐 Germany, Portugal – Remote

🇩🇪 Germany – Remote 🇵🇹 Portugal – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

✨ Scientist

👻 Ghost score 15%

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alqem

WebsiteLinkedInAll Job Openings

2 - 10 employees

🤖 Artificial Intelligence

🔬 Science

🤝 B2B

🔥 Funding within the last year

💰 Pre-Seed Round on 2026-07

Artificial Intelligence • Science • B2B

alqem is a Munich- and Coimbra-founded company that uses artificial intelligence and deep domain expertise to discover and commercialize novel crystalline materials. The team combines decades of experience building materials databases, experimental synthesis and validation, and commercialization to find breakthrough materials for industry; key team members include Hanh Nguyen (CEO), Tiago Cerqueira (CTO, co-builder of the Alexandria materials database) and Milan Allan (professor/experimental physicist). The company emphasizes AI-driven materials discovery and has recent funding news (announced a raise of 8 million in July 2026).

📋 Description

• Design, implement, and continuously improve DFT-based workflows to predict functional properties of crystalline materials • Develop screening strategies and decision criteria to down-select candidates from large search spaces • Guide experimental validation of candidate materials • Own computational plans for assigned discovery programs, including milestones, success criteria, and deliverables • Drive execution of computational discovery programs • Build robust, automated, and reproducible DFT pipelines • Improve computational throughput through AI/ML workflow optimization • Maintain documentation, versioned workflows, and traceable datasets • Collaborate with synthesis and characterization teams to interpret results and troubleshoot discrepancies • Incorporate real-world experimental feedback into improved workflows and models • Communicate results through written summaries and internal presentations • Contribute, where appropriate, to publications, conference abstracts, and grant/partner materials while protecting IP strategy • Contribute to strategic decisions on methodology, prioritization, and platform direction

🎯 Requirements

• PhD in Physics, Chemistry, Materials Science, or a closely related field • 4+ years of deep hands-on DFT experience on crystalline materials • Strong publications and/or impactful open work • Experience with automation and acceleration of DFT, including workflows, convergence optimization, error handling, and high-throughput screening • Experience applying AI/ML methods for materials discovery and search • Strong programming skills in Python or similar • Familiarity with version control, testing, and reproducibility • Ability to work in a fast-moving startup environment with pragmatic prioritization, ownership, and strong collaboration • Experience with one or more of VASP, Quantum ESPRESSO, GPAW, ABINIT, WIEN2k, CASTEP, or comparable codes • Familiarity with phonons, elastic properties, defect calculations, magnetism/spin-orbit coupling, or dielectric/ferroelectric calculations • Strong understanding of thermodynamics, phase stability, and materials databases such as Materials Project, OQMD, and ICSD • Preferred: Hands-on HPC experience with Slurm, job scheduling, profiling, and compute-cost optimization • Preferred: Experience with workflow managers such as ASE, AiiDA, atomate, FireWorks, custodian, Prefect, Airflow, Snakemake, or Nextflow • Preferred: Experience building or maintaining structured materials databases using Postgres, MongoDB, or parquet/lakehouse patterns • Preferred: Experience with materials representations including graph neural networks, CGCNN/MEGNet-like approaches, descriptors, or symmetry-aware models • Preferred: Knowledge of uncertainty quantification and decision-making under uncertainty for experimental planning • Preferred: Experience working with experimental teams or in industrial R&D • Preferred: Track record translating predictions into validated materials and learning cycles

🏖️ Benefits

• Competitive compensation package • Possible equity participation • Opportunity to publish selected results • Contribution to publications, conference abstracts, and grant/partner materials where appropriate • Small, highly interdisciplinary team environment • Culture encouraging ownership, curiosity, problem solving, scientific breakthroughs, collaboration, and professional growth

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