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

Forschungszentrum Jülich; Jülich; On-site; EUR 42,000 - 54,000; Full time

Benefits offered by this job

International research stay

Fixed-term 3 years

30 days leave

TVöD-Bund pay with bonus

International Advisory Service

Job summary

The Institute for Materials Data Science and Informatics (IAS-9) at Forschungszentrum Jülich invites applications for a PhD project embedded in AI and scientific imaging. You will develop core ML methods for electron microscopy and materials discovery, collaborating with data scientists, software engineers, and experimental researchers.

We seek a Master in CS, data science, physics, or related field, with ML background and Python skills (PyTorch/TensorFlow).

Qualifications

• Master or equivalent in CS, data science, applied math, physics, materials science or related field.
• Strong ML and/or computer vision background.
• Interest in representation learning, active learning, uncertainty modelling, or decision-making under constraints.
• Experience with Python and ML frameworks such as PyTorch or TensorFlow.
• Interdisciplinary mindset; microscopy data experience welcome but not required.
• Strong analytical skills and ability to work independently while collaborating in a team.
• Very good command of written and spoken English (CEFR B2 or higher).

Responsibilities

• Develop multi-scale and multi-modal representation learning for scientific imaging data (e.g., SEM, TEM, EBSD).
• Learn representations robust to scale changes, modality shifts, and domain differences across instruments and laboratories.
• Design active learning and experimental design strategies using learned representations to guide data acquisition under cost and uncertainty constraints.
• Build surrogate models that connect imaging-derived representations with downstream physical or functional properties.
• Collaborate closely with experimental partners to integrate decision-making algorithms into real scientific workflows.
• Publish results in high-impact ML and interdisciplinary journals and conferences, and contribute to open-source research software.

Skills

Machine learning

Computer vision

Python

PyTorch

TensorFlow

Interdisciplinary research

Education

Master's degree in CS / Data science / Applied math / Physics / Materials science

Tools

PyTorch

TensorFlow

Job description

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 CEFR)

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 TZA Aachen 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 (JuDocS).
• 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, you 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 BMI website (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.

Links to further information on diversity and equal opportunities:

https://go.fzj.de/equality

and on specific support options: https://go.fzj.de/womens-job-journey

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.