Ph.D Student (m/f/d) - AI-Driven Multiscale Modeling for TMD Materials Synthesis and Characterization (Full-time)

Pdi Berlin; Berlin; Vor Ort; EUR 38.000 - 52.000; Vollzeit

Benefits dieser Stelle

Flexible working hours

Home office

Subsidized travel ticket

Access to HPC centers

International community

Zusammenfassung

The Paul Drude Institute in Berlin seeks a PhD candidate to advance computational modeling of 2D material synthesis, combining DFT, ReaxFF MD, and MLIPs with experimental data. This interdisciplinary role connects atomistic insight to multiscale synthesis conditions.

You will work with an international team on particle-scale to continuum descriptions, utilizing HPC resources and data-driven workflows. The position is embedded in a vibrant research environment in Berlin.

Qualifikationen

• Bachelor's and master's degrees in materials science or physics.
• Interest in computational modeling across atomistic, data-driven, and continuum-scale methods.
• Experience with DFT, reactive MD/ReaxFF, MLIPs or related computational methods is desirable.
• Programming skills in Python, MATLAB, or C++, and familiarity with HPC environments.

Aufgaben

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

Kenntnisse

DFT calculations

ReaxFF

MLIPs

Python

C++

English communication

HPC environments

Ausbildung

Bachelor's degree in materials science or physics

Master's degree in materials science or physics

Tools

DFT software

ReaxFF

MLIP packages

Linux/Unix

Jobbeschreibung

About the position

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.

Your responsibilities

• 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.

Your profile

• Bachelor's and master’s 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.

Position and salary

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.

What we offer

• 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.

What we offer

• 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.

About PDI:

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.

Inclusive and equal opportunity employer

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.