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Forschungszentrum Jülich

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Postdoctoral Researcher in Computational Electron Microscopy and Machine Learning Forschungszentrum Jülich GmbH in Germany

Degree Level

Postdoc

Field of study

Computer Science

Funding

This is a 2-year fixed-term employment contract, with the stated goal of permanent employment afterward. Remuneration is based on pay grade 13 of the TVöD-Bund collective agreement, with salary dependent on qualifications and assigned duties. The position includes a 75% monthly-salary year-end bonus under the collective agreement, capital-forming benefits, an employer-contributed company pension scheme, 30 days of vacation plus additional days off, and other employee benefits.

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Country

Germany

University

Forschungszentrum Jülich

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Keywords

Computer Science
Materials Science
Deep Learning
Physics
ML

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About this position

Forschungszentrum Jülich GmbH is seeking a postdoctoral researcher in computational electron microscopy and machine learning. The position is based within the Institute for Advanced Simulation – Materials Data Science and Informatics (IAS-9), which brings together data analysis, machine learning, materials simulation, research data management, and software development.

The researcher will join the Deep Learning for Electron Microscopy group and work at the interface of electron microscopy and computational methods. The scientific focus is on developing machine-learning approaches grounded in the physics of measurement rather than generic image statistics. The project will address microscopy questions where progress is limited by data analysis and where new computational methods can provide meaningful advances for materials science.

Activities include applying and adapting established machine-learning methods, analysing microscopy data, and developing, training, and evaluating deep-learning models for real-space imaging, 4D-STEM diffraction, and in-situ time-series data. The researcher will construct and characterise experimental and simulated datasets, investigate physically meaningful evaluation criteria, and identify artefacts and failure modes relevant to interpretation of materials.

The role also involves simulation studies using approaches such as multislice and Bloch-wave methods, connecting experiments, theory, and model behaviour. The postdoctoral researcher will collaborate closely with data scientists, software developers, doctoral researchers, and the Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons (ER-C), while contributing microscopy expertise to the group’s method development agenda.

Research outputs will include publications in microscopy, materials science, and machine-learning venues, as well as contributions to open datasets, benchmarks, and software. The researcher will also co-supervise doctoral and master’s students and contribute to research proposals and third-party-funded projects. The research environment benefits from links to the ER-C and the Jülich Supercomputing Centre and is located across the Aachen and Jülich research sites.

Funding details

This is a 2-year fixed-term employment contract, with the stated goal of permanent employment afterward. Remuneration is based on pay grade 13 of the TVöD-Bund collective agreement, with salary dependent on qualifications and assigned duties. The position includes a 75% monthly-salary year-end bonus under the collective agreement, capital-forming benefits, an employer-contributed company pension scheme, 30 days of vacation plus additional days off, and other employee benefits.

What's required

Applicants must have completed a Master's degree and PhD in Physics, Materials Science, or a closely related field. They should have in-depth knowledge of electron microscopy, including image formation, contrast mechanisms, and electron diffraction, with substantial experience in at least one of 4D-STEM, in-situ or operando microscopy, or quantitative HR(S)TEM. Experience with electron microscopy simulations such as multislice or Bloch-wave methods, or quantitative microscopy data-analysis pipelines, is an asset. Practical experience training deep learning models, solid Python programming skills, and working proficiency with PyTorch or an equivalent framework are required. Applicants should be interested in developing methods, possess strong analytical and creative skills, communicate effectively across disciplines, and have very good written and spoken English at least at B2 level according to the CEFR, ideally supported by a certificate.

How to apply

Submit a complete CV including a publication list, a motivation letter explaining your research interests and connection to the position, and all university degree records and certificates through the online application portal. Applications are accepted until the position is successfully filled.

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