development of physics-guided machine-learning models for composition-process-structure-property relationships in magnesium alloys combinati
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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 technology. To this end, materials are tailored and manufacturing processes are designed to conserve resources – from the modeling of material behavior to the development of the material to the finished component. Interested?
Then we are looking forward to receiving your comprehensive application documents (cover letter, CV, transcripts, certificates, etc.) until November 12th, 2026. Apply now 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. Max-Planck-Straße 1 21502 Geesthacht www.hereon.de
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.
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