Introduction: Work at the leading edge of computational materials science
The Max Planck Institute for Sustainable Materials (MPI SusMat), Düsseldorf, invites applications for a PhD / Postdoctoral Researcher (f/m/d) focused on atomistic simulation of material properties. The position sits in a coordinated project exploring structural and chemical atomic complexity—from defect phase diagrams to macroscopic properties such as strength, ductility, and failure mechanisms. Candidates will join an international team and use state-of-the-art methods across electronic-structure theory and large-scale molecular dynamics on high-performance computing systems.
Role focus: Defect phases, complexity, and mechanical behavior
Modern structural and functional materials derive many of their properties from defects and their interactions—vacancies, dislocations, solute clusters, and metastable phases. Consequently, the advertised role centers on:
- Mapping defect phase diagrams: Identify stable and metastable configurations under varying temperature, composition, and stress.
- Linking atoms to mechanics: Connect defect energetics, mobilities, and interactions to yield strength, work hardening, embrittlement, and creep.
- Quantifying chemo-mechanical coupling: Capture how chemistry (segregation, short-range order) alters mechanical responses and failure pathways.
The overarching aim is to move beyond idealized crystals and model realistic, chemically complex alloys—producing predictive insights that guide alloy design and heat treatments.
Methods and toolchain: From DFT to large-scale MD (and beyond)
You will deploy (and help advance) a multiscale modeling stack:
- Electronic-structure calculations (DFT): Compute defectformation energies, segregation trends, migration barriers, and elastic/phonon properties.
- Atomistic simulations (classical MD, accelerated MD): Use LAMMPS or equivalent for dislocation-defect interactions, grain-boundary phenomena, solute drag, and interface kinetics.
- Kinetic and statistical methods: Transition state theory, kinetic Monte Carlo (kMC) or on-the-fly schemes to bridge time scales where MD alone is insufficient.
- Workflow automation and HPC: Reproducible workflows (Python, ASE, FireWorks/Parsl/Snakemake) and efficient use of tier-1 supercomputers; careful uncertainty quantification.
- Electronic-structure calculations (DFT): Compute defect
Depending on background and project needs, you may also engage with machine-learned interatomic potentials, active-learning datasets, or data-centric surrogate models to accelerate screening while retaining physical fidelity.
What you will do: Key responsibilities
Research and modeling
- Build physically consistent models of defect structures and complex interfaces in technologically relevant alloys.
- Simulate mechanism-level processes (e.g., solute-dislocation interactions, emission/absorption at interfaces) and upscale the results to property predictions.
- Develop computational protocols that balance accuracy and throughput, validating core predictions against experiment where data exist.
Scientific communication
- Produce high-quality figures, data tables, and methodological appendices for publications.
- Present results at group meetings, seminars, and international conferences; contribute to collaborative reports and project milestones.
Reproducibility and data stewardship
- Maintain version-controlled repositories, input decks, and post-processing scripts.
- Curate datasets with metadata, ensuring reuse across project partners.
What you will bring: Qualifications and mindset
Minimum background (one of the following)
- PhD applicants: A strong master’s degree in Materials Science, Physics, Mechanical/Metallurgical Engineering, Chemistry, or closely related fields with substantive computational exposure.
- Postdoctoral applicants: A completed PhD with publications in atomistic simulations, defect physics, computational metallurgy, or computational condensed-matter.
Technical strengths (indicative)
- Experience with DFT (e.g., VASP, Quantum ESPRESSO, FHI-aims) or MD (e.g., LAMMPS); comfort on Linux/HPC clusters.
- Proficiency in Python for analysis and workflow automation; familiarity with ASE, NumPy/Pandas, and plotting libraries.
- Understanding of crystal defects, thermodynamics/kinetics, and structure–property relationships in metals or complex alloys.
Research habits
- Clear scientific writing, meticulous record-keeping, and an inclination toward open, reproducible workflows.
- Collaborative mindset, willingness to co-design experiments with partners, and curiosity to explore new theories and tools.
Training environment: Why MPI SusMat?
Max Planck institutes combine long-term fundamental research with cutting-edge infrastructure.
- Interdisciplinary teams spanning computation, microscopy, scattering, and mechanical testing—ideal for simulation–experiment cycles.
- Access to high-performance computing and shared codebases, plus internal seminars that sharpen both theory and communication.
- A supportive setting for early-career researchers, including mentoring on publishing, project management, and career development.
Career pathways: Where this role can take you
- Academia: Strengthen your track record in computational materials science, preparing for competitive postdoc roles or junior group leader tracks.
- R&D labs and industry: Translate atomistic insights into alloy design, heat-treatment optimization, and process troubleshooting.
- Scientific software and HPC: Build expertise relevant to workflow engineering, potential development, and research computing leadership.
Application guide: How to submit a convincing dossier
1) Prepare a targeted CV
Keep it to two or three pages. Feature methods (DFT/MD/kMC), software stacks, HPC familiarity, and top three outputs with links (preprints, code, datasets).
2) Write a precise cover letter (≤1 page)
Explain why atomistic simulation of defects is the right problem for you now. Then, map two specific skills to project
3) Provide a short research statement (1–2 pages)
Outline two feasible work packages you could deliver in year one:
- WP-A (accuracy-first): e.g., DFT barriers for solute migration and a training set for an ML potential.
- WP-B (scale-first): e.g., MD/kMC pipeline for solute drag and strengthening models across temperatures.
State risks, fallbacks, and validation data you would seek.
4) References and artifacts
List two to three referees; include code or data artifacts (if permissible) that demonstrate reproducibility and numerical care.
5) Submission and timeline
Apply via the official external platform linked from the institute’s Job Offers page. Prepare to upload PDFs within the stated size limits and complete any form-based fields carefully. If a preferred start window is given, indicate availability and visa considerations succinctly.
Interview preparation: Show depth, not just breadth
- Whiteboard the physics: Defect formation energies, migration barriers, and how they feed into diffusion-controlled processes.
- Discuss numerical choices: Supercell sizes, k-point meshes, thermostats/barostats, potential selection, time-step sensitivity, and uncertainty quantification.
- Bridge scales: Explain how atomistic results inform dislocation-based models or continuum constitutive laws.
Living and working in Düsseldorf
Düsseldorf offers an international community, efficient public transit, and proximity to research hubs across NRW and neighboring countries. The Rhine-Ruhr region hosts a dense network of universities and research centers, making collaboration and conference travel straightforward.
Equality, diversity, and visa support
Max Planck institutes are committed to equal opportunity and family-friendly policies. International applicants typically receive relocation and visa guidance from dedicated administrative teams. Compensation and contracts follow standard Max Planck or partner-university frameworks appropriate to your career stage.
Final steps: Ready to apply?
If your expertise sits at the intersection of defect physics and computational modeling, this position gives you the resources, mentorship, and scale to tackle chemically complex, structurally intricate materials with real-world relevance. Review the official Open Positions listing, follow the link to the application portal, and submit a targeted, technically rigorous dossier.
Item | Details |
Host Institute | Max Planck Institute for Sustainable Materials (MPI SusMat), Düsseldorf (Germany) |
Position | PhD or Postdoctoral Researcher (f/m/d) — Atomistic Simulation of Material Properties |
Scientific Focus | Defect phase diagrams, chemical/structural complexity, dislocation–defect interactions, chemo-mechanical coupling, property prediction (strength, ductility, failure) |
Methods / Toolchain | DFT (e.g., VASP/QE/FHI-aims), classical/accelerated MD (e.g., LAMMPS), kMC/statistical methods, Python/ASE workflows, HPC, optional ML interatomic potentials |
Key Objectives | Link atomistic energetics and kinetics to mesoscale behavior; develop reproducible simulation workflows; validate mechanisms against experiment; generate design-relevant insights |
Minimum Eligibility | PhD track: strong Master’s in Materials Science, Physics, Mech, Metallurgy, Chemistry or related, with computational exposure. Postdoc: PhD in atomistic simulations/defect physics/metallurgy/condensed matter |
Desired Skills | Linux/HPC proficiency; Python for analysis/automation; understanding of defects, thermodynamics/kinetics; clean scientific writing/data stewardship |
Core Responsibilities | Build/parameterize models; run DFT/MD/kMC; analyze/visualize data; prepare publications and talks; maintain version-controlled, reproducible workflows |
Application Materials | Targeted CV (2–3 pages), cover letter, short research statement (1–2 pages, two feasible work packages), references (2–3), links to code/data (if permissible) |
Submission Route | Apply via the institute’s official job posting → external application portal (PDF uploads within specified size limits) |
Evaluation Emphasis | Method readiness, clarity of mini-work packages, reproducibility discipline, numerical care/uncertainty, ability to bridge atomistics to properties |
Training Environment | Interdisciplinary teams; strong HPC access; simulation–experiment loops; mentoring on publishing, project mgmt., and career development |
Career Outcomes | Academia (postdoc/junior PI), R&D labs and alloy design, scientific software/HPC workflow engineering |
EDI & Mobility | Equal opportunity employer; administrative support for relocation/visa; family-friendly policies |
Location Perks | International city with efficient transit; dense NRW research network; easy travel across Rhine-Ruhr and EU hubs |
Next Steps | Review official listing; tailor dossier to defect physics and multiscale goals; submit early and follow portal instructions precisely |
Official Link: Click here
Frequently Asked Questions (FAQs)
It investigates defect phase diagrams, dislocation–defect interactions, and chemo-mechanical coupling to predict material strength, ductility, and failure across chemically complex alloys.
Apply with a strong master’s in materials science, physics, mechanical/metallurgical engineering, or chemistry, plus demonstrable computational experience and readiness for HPC-based research.
You need a completed PhD in atomistic simulations, defect physics, computational metallurgy, or condensed-matter theory, along with relevant publications and reproducible workflows.
Prefer experience with DFT codes (VASP, Quantum ESPRESSO, FHI-aims) and MD packages (LAMMPS). Additionally, use Python, ASE, and version control for automation and analysis.
It links defect energetics and kinetics to mesoscale behavior using DFT, MD, and kMC pipelines, then validates predictions against experimental data whenever available.
Demonstrate Python proficiency, clean data handling, uncertainty quantification, and robust visualization. Moreover, maintain documented, version-controlled repositories for reproducibility.
Not strictly; however, familiarity helps. You may build or deploy ML potentials and active-learning datasets to accelerate screening without sacrificing physical fidelity.
Expect technologically relevant metals and alloys with compositional and structural complexity, including interfaces, grain boundaries, and solute-rich microstructures.
Explain defect formation energies, migration barriers, supercell choices, k-meshes, thermostats, potential selection, time-step sensitivity, and how atomistic outputs inform continuum models.
