PhD position on Physics-Guided Digital Twin Development for Composition-Process-Structure-Property Modeling of Magnesium Alloys

Helmholtz Association of German Research Centres; Deutschland; On-site; EUR 18,000 - 23,000; Part time

Benefits offered by this job

International Welcome Office

6 weeks holiday per year

Public sector pay grade EntgO Bund

Flexible working options

Deutschlandticket (job ticket)

Job summary

Helmholtz-Zentrum Hereon in Geesthacht offers a 4-year PhD position in computational materials engineering, ML, and digital twins for magnesium alloys. You will integrate experimental data and physics-based models to establish structure-property relationships within an ICME framework.

The project emphasizes physics-guided surrogate models and collaboration across mechanical engineering, materials science, and AI. Equal opportunity is encouraged for qualified applicants.

Qualifications

• Master's degree in a related discipline (e.g., mechanical engineering, materials science, data science).
• Strong interest in computational materials science, digital twins, and ML.
• Good programming skills in Python and experience with ML libraries.

Responsibilities

• Develop physics-guided ML models for composition-process-structure-property relationships in magnesium alloys.
• Combine crystal plasticity, phase-field, precipitation, and ML into a unified digital twin framework.
• Implement simulation and ML workflows in Python (e.g. PyTorch, Scikit-Learn).
• Incorporate experimental microstructure data from microscopy and XRD for validation.
• Publish results in leading journals and contribute to ICME and digital-twin methods.

Skills

Python programming

Machine learning

Analytical skills

Independent work

English proficiency

Education

Master's degree

Tools

Python

PyTorch

Scikit-Learn

Job description

Organisation/Company Helmholtz-Zentrum Hereon Research Field All Researcher Profile First Stage Researcher (R1) Application Deadline 11 Nov 2026 - 23:00 (UTC) Country Germany Type of Contract To be defined Job Status Other Offer Starting Date 15 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Institute of Materials and Process Design

Area of research:

PHD Thesis

Part-Time Suitability:

The position is suitable for part-time employment.

15.10.2026

Job description:PhD position on Physics-Guided Digital Twin Development

Referenzcode: 1057

Arbeitsort: Geesthacht

Bewerbungsfrist: 12.11.2026

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

Our offer

• 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

Severely disabled persons and those equaling severely disabled persons who are equally suitable for the position will be considered preferentially within the framework of legal requirements.

This research center is part of the Helmholtz Association of German Research Centers. With more than 42,000 employees and an annual budget of over € 5 billion, the Helmholtz Association is Germany's largest scientific organisation.