Helmholtz-Zentrum Hereon

PhD position on Physics-Guided Digital Twin Development for Composition-Process-Structure-Property M

Geesthacht, Schleswig-Holstein, Germany

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Role family: Engineering
Seniority: Mid level
Country: DE
Work mode: On-site / unstated
First seen by hirly: 16 Oct 2026

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the posting

Cutting-edge Research for a Changing World PhD position on
Physics-Guided Digital Twin Development for
Composition-Process-Structure-Property Modeling of Magnesium Alloys
Reference code: 1057 - 2026/WD 1 Commencement date: as soon as
possible Work location: Geesthacht Application deadline: November
12th, 2026 The Institute of Material and Process Design at the
Helmholtz-Zentrum Hereon is offering a 4-year PhD position in the
field of computational materials engineering, machine learning, and
digital twins for advanced lightweight materials. The focus of the PhD
project will be the development of a physics-guided digital twin
framework for magnesium alloys by integrating experimental
characterization, crystal plasticity, phase-field modeling,
precipitation modeling, and machine learning approaches to establish
quantitative composition-process-structure-property relationships.
Building upon recently developed machine-learning approaches for
structure-property prediction in extruded magnesium alloys, the
project aims to advance toward a next-generation Integrated
Computational Materials Engineering (ICME) framework capable of
predicting microstructure evolution and resulting mechanical
properties across previously unseen processing conditions and material
states. The candidate will work closely with interdisciplinary teams
combining expertise in magnesium alloy development, thermomechanical
processing, crystal plasticity, phase-field modeling, machine
learning, and advanced experimental characterization. The objective of
the project is to bridge the gap between purely data-driven materials
modeling and mechanistic microstructure simulations by combining
crystal plasticity, dynamic recrystallization, precipitation kinetics,
and machine learning into a unified digital-twin framework. The
resulting methodologies shall enable accelerated alloy and process
design by leveraging experimentally validated physics-guided surrogate
models with improved extrapolation capabilities beyond the limits of
conventional machine-learning approaches. This work directly
contributes to the digital transformation of materials development and
manufacturing technologies for future lightweight engineering
applications. Equal opportunity is an important part of our personnel
policy. We would therefore strongly encourage qualified women to apply
for the position. Your tasks development of physics-guided
machine-learning models for composition-process-structure-property
relationships in magnesium alloys combination of crystal plasticity,
phase-field, precipitation, and machine-learning approaches into a
unified digital twin framework implementation of simulation and
machine-learning workflows in Python (e.g. PyTorch, Scikit-Learn,
etc.) incorporation of experimental microstructure and texture
characterization data obtained from optical microscopy, SEM, EBSD,
XRD, and mechanical testing investigation of uncertainty
quantification and explainable artificial intelligence methods (XAI)
for materials modeling validation of computational predictions in
collaboration with experimental and modeling researchers publication
and presentation of scientific results in leading international
journals, conferences, and workshops active contribution to the
establishment of next-generation ICME and digital-twin methodologies
for sustainable lightweight materials design Your profile master’s
degree in mechanical engineering, materials science, computational
engineering, physics, applied mathematics, data science, or a related
discipline strong interest in computational materials science, digital
twins, and machine learning good programming skills in Python
experience with machine-learning methods and common scientific Python
libraries is desirable knowledge of computational mechanics, finite
element methods, crystal plasticity, phase-field modeling, or
thermodynamic simulations is advantageous experience in analysis of
experimental materials-science data is beneficial ability to work
independently as well as within an interdisciplinary research team
strong analytical and problem-solving skills highly proficient in
spoken and written English motivation to conduct high-quality research
at the interface of materials science, mechanical engineering, and
artificial intelligence We offer you an exciting and varied job in a
research centre with around 1,000 employees from more than 60 nations
an International Welcome Office with guest accommodation 6 weeks
holiday per year; company holidays between Christmas and New Year's
Day social benefits under the collective agreement for the German
public sector and remuneration up to pay grade 13 (EntgO Bund) very
good work-life balance; options for mobile and flexible working a
well-connected research campus, good networking opportunities, a
subsidized Deutschlandticket (job ticket) subject to certain
conditions, and charging facilities for electric vehicles individual
opportunities for training and development an excellent technical
infrastructure and modern workplace equipment PhD Buddy Program a
family-friendly working environment with childcare options, e.g. a
daycare center close to the workplace workplace health promotion
programmes, e.g. an Employee Assistance Program (EAP) corporate
benefits a varied offer in the canteen on campus Helmholtz-Zentrum
Hereon The Helmholtz-Zentrum Hereon conducts cutting-edge
international research for a changing world: Around 1,000 employees
contribute to the tackling of climate change, the sustainable use of
the world's coastal systems and the resource-compatible enhancement of
the quality of life. From fundamental research to practical
applications, the interdisciplinary research spectrum covers a unique
range. Institute of Material and Process Design The Institute of
Material and Process Design is dedicated to the sustainable and
ecological development of innovative materials and manufacturing
processes, especially for the transportation sector and medical
techno

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