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

Forschungszentrum Jülich; Aachen; Presencial; EUR 40.000 - 45.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Creative work environment

Networking opportunities

Professional development

30 days annual leave

Flexible working hours

International support

Descripción de la vacante

Forschungszentrum Jülich in Aachen invites a highly motivated PhD candidate to advance foundational AI research at the interface of artificial intelligence, scientific imaging, and materials science. You will contribute to data science and machine learning activities in IAS-9, developing core AI methods with applications to electron microscopy and materials discovery.

You will work within a team of data scientists, software engineers, and experimental researchers on multi-modal representation

Formación

• 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.
• Very good command of written and spoken English (CEFR B2 or higher) with certificate preferred.

Responsabilidades

• Develop multi-scale and multi-modal representation learning methods for scientific imaging data (SEM, TEM, EBSD).
• Learn representations robust to scale changes, modality shifts, and domain differences across instruments and labs.
• Design active learning and experimental design strategies to guide data acquisition under cost/uncertainty constraints.
• Build surrogate models linking imaging representations with downstream properties.
• Collaborate with experimental teams to integrate decision-making algorithms into workflows.

Conocimientos

Machine learning

Python

Active learning

Uncertainty modeling

Decision making under constraints

English proficiency

Educación

Master's degree in CS/Data Science/Applied Math/Physics/Materials Science or related field

Herramientas

PyTorch

TensorFlow

Descripción del empleo

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.

The following links provide further information on diversity and equal opportunities: https://go.fzj.de/equality and on specific support options: https://go.fzj.de/womens-job-journey