Postdoctoral Researcher – Computational Electron Microscopy and Machine Learning, Germany

Summary

The Institute for Advanced Simulation – Materials Data Science and Informatics (IAS-9) at Forschungszentrum Jülich GmbH is seeking a highly motivated Postdoctoral Researcher. This position focuses on the intersection of computational electron microscopy and machine learning, aiming to develop innovative methods for analyzing microscopy data and extracting new scientific insights. The role involves identifying critical questions in electron microscopy that can be addressed through novel machine learning approaches, developing and validating these methods against real experimental data, and contributing to a multidisciplinary research environment.

Designation

Postdoctoral Researcher

Research Area

• Computational Electron Microscopy
• Machine Learning
• Materials Data Science and Informatics
• Deep Learning for Electron Microscopy

Location

Aachen and Jülich, Germany (Initially primarily in Aachen)

Eligibility/Qualification

Education

• Completed Master’s degree and PhD in Physics, Materials Science, or a closely related field.

Experience & Skills

• In-depth working knowledge of electron microscopy, including image formation, contrast mechanisms, and electron diffraction.
• Substantial experience in at least one of the following: 4D-STEM, in-situ or operando microscopy, or quantitative HR(S)TEM.
• Experience with electron microscopy simulations (e.g., multislice or Bloch-wave methods) or developing quantitative data analysis pipelines for microscopy data is a strong asset.
• Practical, hands-on experience in training deep learning models.
• Solid programming skills in Python.
• Working proficiency with PyTorch or an equivalent framework.
• Clear interest in developing methods rather than merely applying them, and motivation to deepen machine learning expertise.
• Excellent communication skills across disciplinary boundaries.
• Strong analytical skills and creativity.
• Very good command of written and spoken English (at least B2 level according to CEFR), ideally supported by a certificate.

Job Description

This position is at the interface between electron microscopy and machine learning, focusing on shaping how machine learning is developed for electron microscopy. You will work within the Deep Learning for Electron Microscopy group, collaborating with data scientists, software developers, doctoral researchers, and the Ernst Ruska-Centre (ER-C).

Key Tasks

• Conducting research at the interface of electron microscopy and machine learning, including application and adaptation of established methods, data analysis, and dissemination of results.
• Identifying open questions in electron microscopy where progress is limited by analysis and where new machine learning methods offer genuine scientific gain.
• Developing, training, and evaluating deep learning models for microscopy data (real-space imaging, 4D-STEM diffraction, in-situ time series), including constructing and characterizing experimental and simulated datasets.
• Establishing physically meaningful evaluation criteria for these models, understanding what constitutes a correct answer, identifying artefacts, and determining relevant failure modes for materials science.
• Conducting and interpreting simulation studies (e.g., multislice, Bloch-wave methods) to connect experiment, theory, and model behavior.
• Participating in the scientific agenda of the collaboration with the ER-C, translating microscopy questions into method development and model results into meaningful statements for microscopists.
• Publishing in microscopy and materials science journals, as well as at machine learning venues, and contributing to open datasets, benchmarks, and software.
• Co-supervising doctoral and master students.
• Contributing to proposal writing and the group’s third-party funded projects.

How to Apply

To apply, please submit the following documents:

• A complete CV, including a publication list.
• A letter of motivation describing your research interests and how they connect to this position.
• All university degree records and certificates.

Forschungszentrum Jülich GmbH welcomes applications from people with diverse backgrounds and promotes a diverse and inclusive working environment with equal opportunities.

Last Date for Apply

The position will be published until it is successfully filled.

Apply Link

Online Application Portal

Additional Information