The PhD project is methodologically independent, with the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists, software developers and domain scientists on, e.g.:
- Developing self-supervised learning frameworks to extract features from unlabeled high-resolution microscopy data
- Training and evaluating segmentation models for detecting and characterizing defects such as dislocations
- Applying generative models (e.g., GANs, diffusion models) to augment microscopy datasets
- Investigating domain adaptation techniques across different imaging modalities
- Collaborating closely with experimental partners to validate methods and integrate tools into existing workflows
- Disseminating findings through scientific publications, international conferences, and open-source contributions.licable enter further tasks of the position
Table of Content
Summary
Subscribe for Scholarship Alert!
Benefits
- A dynamic, interdisciplinary research environment at the forefront of materials informatics
- Comprehensive training courses and individual opportunities for personal and professional further development. A structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/en/judocs
- The opportunity to attend national and international conferences
- Optimal conditions for work-life balance, including a family-friendly corporate policy, flexible working hours, the option for home office days, and 30 vacation days per year
- A creative work environment at a leading research facility, located on an attractive research campus at the TZA Aachen https://tza-aachen.de and the Forschungszentrum Jülich
- Flexible working hours in a full-time position with the option of slightly reduced working hours ( https://go.fzj.de/near-full-time )
- Targeted services for international employees, e.g. through our International Advisory Service
Requirements
- A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field
- Prior experience in computer vision, deep learning, or signal processing; familiarity with microscopy data is an asset but not required
- Interest in foundational machine learning research with applied impact in scientific imaging
- Demonstrated proficiency in Python and experience with ML/DL frameworks like PyTorch or TensorFlow
- Strong analytical and communication skills, creativity, and the ability to work independently while collaborating in a team-oriented environment
Application Deadline
Not SpecifiedHow To Apply
Are you qualified and interested in this opportunity? Kindly go to
Forschungszentrum Jülich on www.fz-juelich.de to apply
For more information, kindly visit Forschungszentrum Jülich schoalrship webpage.
