Postdoc position; f​/m​/d Developing Predictive Theoretical Framework D materials Design and Synthesis

About the position

We invite applications for a Postdoctoral Researcher to develop advanced
computational approaches to design and synthesize 2D materials grown by
vapor-phase techniques. This three-year position is part of
the NSF–DFG DMREF project “AI-Driven Platform for 2D Materials Synthesis and Discovery”.
The project integrates computational materials science, autonomous experimentation,
and AI to develop a predictive framework for 2D-material synthesis. The research
will span the full growth process—from gas-phase precursor chemistry and
surface reactions to thin-film growth and resulting material properties.
The successful candidate will combine first-principles calculations (DFT), reactive
molecular dynamics (ReaxFF), and machine-learning interatomic potentials (MLIPs)
to develop multiscale, high-throughput workflows for reactive growth environments.
The work will be closely integrated with experiments, machine learning/data science,
and micro- to mesoscale modelling, providing opportunities to lead high-impact
interdisciplinary research in predictive materials synthesis.

• Lead first-principles DFT calculations to investigate 2D materials-related optical,
  electronic, and structural properties.
• Develop, validate, and apply machine-learning interatomic potentials (MLIPs) for
  large-scale atomistic simulations of reactive materials-growth processes.
• Integrate DFT, ReaxFF, and MLIP simulations into multiscale and high-throughput
  computational workflows.
• Perform simulations on national and international HPC infrastructures, including
  hybrid CPU/GPU architectures, and optimize computational workflows for
  large-scale studies.
• Work closely with experimental, machine-learning/data-science, and micro- to
  mesoscale modeling teams to connect simulations with experimental observations
  and synthesis conditions.
• Analyze complex simulation and experimental datasets; experience with
  machine-learning approaches for image processing and analysis is an advantage.
• Mentor Master’s/PhD students in computational techniques, model development,
  and project planning.
• Contribute to/lead manuscripts actively and user/grant proposals, and present
  results in group meetings and at conferences.

• PhD in computational materials science and computational physics.
• Computational expertise:
  Strong expertise in first-principles methods,
  particularly Density Functional Theory (DFT), and machine-learning interatomic
  potentials (MLIP), or applying machine-learning methods to materials
  problems, is a strong advantage.
• Programming and workflow development: proficiency in scientific programming,
  preferably Python, with experience developing automated simulation workflows,
  high-throughput frameworks, or computational pipelines.
• HPC Expertise: demonstrated experience with high-performance computing,
  including parallel computing, workload/job scheduling, and running or optimizing
  large-scale simulations on CPU and/or GPU architectures.
• Communication and mentorship: good written and oral communication skills,
  with enthusiasm for mentoring students and working in an international,
  interdisciplinary research environment.

This position is available immediately and is limited to 3 years.
Salary and benefits are according to the Treaty for German public service (TVöD Bund)
to a level of E13 (100%), taking work experience and special professional skills into account.

• Supportive environment with experts for various scientific sub-fields.
• Modern office located in the heart of Berlin with excellent public transport
  connections and a subsidized travel ticket.
• Access to national and international HPC centers with modern hybrid CPU/GPU
  architectures.
• International and culturally diverse community.

• Close collaboration with a…