Postdoc position (f/m/d) – Developing Predictive Theoretical Framework for 2D materials Design and Synthesis (Full Time) - #2687036

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 modeling, 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 nationa/international team integrating experiments,
  computational materials science, machine learning, data science, and
  micro- to mesoscale continuum modeling. 

The Paul Drude Institute is part of the Forschungsverbund Berlin e.V. and a member
of the Leibniz Association. 
We are a globally recognized research institution specializing in the
development of novel functional materials through molecular beam epitaxy.
The institute carries out basic and applied research at the nexus of materials science,
condensed matter physics, and device engineering.

With approximately 100 employees and more than 15 nationalities, PDI is committed
to building a talented, inclusive, and culturally diverse workforce. We understand that
our shared future is guided by basic principles of fairness and mutual respect.

As an equal opportunity and family-friendly employer, we offer highly flexible
employment conditions, such as flexible working hours, parental leave, and
home office, and we strive to create a family- and life-conscious working environment.

Among equally qualified applicants, preference will be given to candidates from
marginalized groups. That means, we welcome every qualified application, regardless
of sex and gender, origin, nationality, religion, belief, health and disabilities, age or
sexual orientation.

PDI follow our gender equality plan, so we want to engage women* to apply at
PDI to balance the gender ratio in science. Disabled applicants with equal qualification
and aptitude will be given preferential consideration.

Please upload your application as a single PDF with the title
of the position in the subject line. The document should include:

• Dedicated cover letter.
• CV
• Transcript of Diplomas
• Letter(s) of recommendation
• Contact information
  of references (if existing).
• Publication list (if existing).

For more information about the project, please contact Prof. Nadire Nayir
([email protected]).