Ph.D Student (mfd) AI-Driven Multiscale Modeling for TMD Materials Synthesis and Characterization (Full-time)

This fully funded PhD position is part of the NSF-DFG DMREF project
AI-Driven Platform for 2D Materials Synthesis and Discovery an
international effort to establish a predictive framework for the synthesis of
two-dimensional materials. By integrating computational materials science
with autonomous experimentation and artificial intelligence the project aims
to uncover how synthesis conditions govern material formation and use this
knowledge to guide the discovery and controlled growth of 2D materials.

The PhD candidate will focus on computational modeling of synthesis and
characterization of 2D materials across multiple length and time scales
with a particular focus on transition-metal dichalcogenides (TMDs). The
research will combine density functional theory (DFT) ReaxFF reactive
molecular dynamics and machine-learning interatomic potentials (MLIPs)
to reveal the mechanisms underlying nucleation growth and structural
evolution and to develop predictive models that connect atomistic mechanisms
with experimentally accessible synthesis conditions.

The position is embedded in a highly interdisciplinary collaboration spanning
materials synthesis and characterization computational materials science
machine learning and continuum fluid dynamics at the micro- and mesoscales.
This environment will allow the candidate to connect fundamental atomistic insight
with experiments and larger-scale descriptions of the synthesis environment
developing a broad multiscale and multiphysics perspective on materials growth-from
electronic structure and chemical reactions to experimentally observed
synthesis processes.

• Perform and analyze density functional theory (DFT) calculations relevant to
  TMD-material synthesis and surface processes.
• Conduct reactive simulations using ReaxFF to investigate precursor gas-phase
  chemistry surface reactions and growth mechanisms.
• Perform machine-learning interatomic potential (MLIP) simulations and connect
  high-fidelity atomistic calculations with larger-scale models.
• Integrate simulation results with experimental observations and
  machine-learning/data-driven workflows within digital/physical-twin approaches.
• Collaborate with an interdisciplinary international team spanning synthesis
  experiments computational materials science AI/data science and
  continuum fluid dynamics.
• Use national and international HPC resources including hybrid CPU/GPU systems
  and develop reproducible workflows for analysis publications and presentations.

• Bachelors and masters degrees in materials science or physics.
• A strong interest in computational modeling of materials and in learning across
  atomistic data-driven and continuum-scale methods.
• Prior experience with DFT reactive molecular dynamics/ReaxFF MLIPs atomistic simulation or related computational methods is highly desirable.
• Background in Programming skill using such as Python MATLAB or C and
  familiarity with Linux/Unix environments and high-performance computing (HPC)
  systems is advantageous.
• Effective communication skills both written and verbal in English are essential
  for presenting research findings and collaborating with team members.
• A genuine enthusiasm for contributing to cutting-edge research in the field of
  materials science.
• A self-motivated personality with a strong curiosity for working in a multi-disciplinary
  team environment on scientifically challenging problems. Team-oriented with the
  ability to collaborate effectively with others.

This position is available immediately. Salary and benefits are according to the
Treaty for German public service (TVöD Bund) to a level of E13 (75%) 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.

• Unique theory/simulation capabilities
• Access to national and international HPC centers with modern hybrid CPU/GPU architectures.
• Supportive environment with experts for various scientific sub-fields.
• International and culturally diverse community.
• Location in the heart of Berlin with excellent public transport connections and a subsidized travel ticket.
• Close collaboration with a national and international team integrating experiments computational materials science machine learning and data science as well as micro- to mesoscale continuum modeling.

The Paul Drude Institute is part of the Forschungsverbund Berlin e.V. and a member
of the Leibniz 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 anequal 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 send your application as a single PDF file to Susanne Sawert
(she/her) at by Sept 30 2026 with the title
of the position in the subject line. The document should include:

a dedicated cover letter

CV

publication list (if exists)

contact information of two references (if exists)

diploma(s)

notes transcript(s).