PhD Position - Representation and Active Learning for Multi-Sc...

Forschungszentrum Jülich GmbH P-VA Personalabrechnung; Jülich; On-site; EUR 42,000 - 54,000; Full time

Benefits offered by this job

30 days annual leave

Flexible working hours

International Advisory Service

Job summary

Das IAS-9 am Forschungszentrum Jülich bietet eine Promotionsstelle für Representation Learning und Active Learning in der wissenschaftlichen Bildgebung. Sie arbeiten in einem interdisziplinären Team an Methoden für SEM, TEM und EBSD in der Materialforschung.

Schwerpunkt ist robuste Repräsentationen, daten‑effiziente Entscheidungsfindung und Surrogatmodelle, eingebettet in reale Experimente. Bewerber haben Masterabschluss in Informatik, Physik, Mathematik oder Materialwissenschaften und gute

Qualifications

• Master oder gleichwertig in Informatik, Mathematik, Physik, Materialwissenschaft oder verwandtem Feld.
• Fundierte Kenntnisse in ML/Computer Vision.
• Interesse an Representation Learning, Active Learning, Unsicherheitsmodellierung.
• Programmierkenntnisse in Python; PyTorch oder TensorFlow.
• Teamfähigkeit und eigenständige Arbeitsweise.
• Gute Englischkenntnisse (B2+).

Responsibilities

• Weiterentwicklung von Representation Learning-Methoden für wissenschaftliche Bildgebung (SEM, TEM, EBSD).
• Entwicklung daten‑effizienter Entscheidungsstrategien und Active Learning.
• Entwicklung von Surrogatmodellen zur Verbindung imaging‑basierter Repräsentationen mit physikalischen Eigenschaften.
• Zusammenarbeit mit Experimentatoren zur Integration von Entscheidungsalgorithmen in Arbeitsabläufe.
• Beitrag zu Publikationen und Open‑Source‑Software.

Skills

Python

ML/Computer Vision

Active Learning

Representation Learning

Education

Master oder gleichwertig

Tools

PyTorch

TensorFlow

Job description

PhD Position - Representation and Active Learning for Multi-Scale Scientific Imaging

The Institute for Materials Data Science and Informatics (IAS-9) develops advanced Machine Learning & Artificial Intelligence methods tailored to challenges in the physical sciences and engineering, bridging data-driven approaches with domain knowledge to push the boundaries of scientific discovery. Our group brings together ML engineers, AI researchers, data scientists, research software engineers, and domain scientists with a shared focus on scientific machine learning. Together, we develop and apply ML methods to tackle key challenges in the physical sciences and engineering: from accelerating simulations with surrogate models to extracting insights from complex imaging data, and building approaches that transfer across domains.

In addition, we benefit from a strong connection to the Ernst-Ruska-Centre for Electron Microscopy and to the Jülich Supercomputing Center. We are particularly interested in advancing foundational machine learning methods for scientific imaging, with a focus on representation learning and data-efficient decision‑making across heterogeneous data sources.

Your Job

The PhD project is methodologically independent and embedded in a multidisciplinary research environment at the interface of artificial intelligence, scientific imaging, and materials research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team of data scientists, software engineers, and experimental researchers on topics including:

• Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM, TEM, EBSD).
• Learning representations that are robust to scale changes, modality shifts, and domain differences across instruments and laboratories.
• Designing active learning and experimental design strategies that use learned representations to guide data acquisition under cost and uncertainty constraints.
• Building surrogate models that connect imaging-derived representations with downstream physical or functional properties.
• Collaborating closely with experimental partners to integrate decision‑making algorithms into real scientific workflows.
• Publishing results in high-impact machine learning and interdisciplinary journals and conferences, and contributing to open‑source research software.

The developed methods will be validated using large-scale electron microscopy data from collaborative research projects, including an EU-funded project on sustainable steel development, while maintaining a clear focus on fundamental AI research questions.

Your Profile

We are looking for a highly motivated candidate with a strong interest in foundational machine learning research and its application to real-world scientific problems. You should bring:

• A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field.
• Solid background in machine learning and/or computer vision.
• Interest in representation learning, active learning, uncertainty modeling, or decision‑making under constraints.
• Experience with Python and modern ML frameworks such as PyTorch or TensorFlow.
• Curiosity for interdisciplinary research; prior experience with scientific or microscopy data is welcome but not required.
• Strong analytical skills, scientific creativity, and the ability to work independently while collaborating in a team environment.
• Very good command of written and spoken English with extensive vocabulary is required (at least B2 level according to the ), ideally supported by a certificate confirming the language level.

Our Benefits for You

We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:

• A creative work environment: A leading research facility, located on an attractive research campus at the and the Forschungszentrum Jülich. The opportunity to conduct exciting research in an international and multidisciplinary environment with outstanding infrastructure and to strengthen your reputation in a dynamic and highly active research field
• Networking & Exchange: The opportunity to participate in international conferences and project meetings as well as an opportunity for a fully funded extended international research stay
• Knowledge & Development: Your professional development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates .
• Annual Leave: You get 30 days of annual leave
• Work‑life balance: We offer flexible working hours to help you balance your professional and personal life. You also have the option of flexible working (in terms of location), which is generally possible after consultation and in line with upcoming tasks and (on‑site) appointments
• Fair remuneration: Depending on your qualifications and assigned responsibilities, we will be classified according to pay group 13 (80%) of the TVöD‑Bund. In addition to the basic salary, there is an additional year‑end bonus under the collective pay agreement amounting to 75% of a monthly salary, as well as capital‑forming benefits. All information about the TVöD‑Bund collective agreement can be found on the (pay scale table on page 74 of the PDF download).
• Fixed‑term: The position is initially for a fixed term of 3 years
• Support for international employees: Our International Advisory Service makes it easier for international employees to get started

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.

The following links provide further information on diversity and equal opportunities: and on specific support options:

Place of Employment: Aachen und Jülich

Start Date:To the next possible date

Salary: Pay group 13 (80%) TVöD‑Bund

Application Deadline:The job will be advertised until the position has been successfully filled.