PhD Position – Analysis in Electron Microscopy: This PhD position offers an exciting opportunity to engage in groundbreaking research at the intersection of deep learning and electron microscopy. The role focuses on developing machine learning methods for analyzing lattice defects, essential for material behavior and reliability.
PhD Position – Deep Learning for Dislocation Analysis in Electron Microscopy
Designation
PhD Researcher
Research Area
Deep Learning, Electron Microscopy, Materials Science, Data Science
Location
Jülich/Aachen, Germany
| Detail | Description |
|---|---|
| Position Type | Fixed-term of 3 years |
| Salary | 75% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) |
| Additional Benefits | 60% of a monthly salary as special payment (“Christmas bonus”), flexible working hours, family-friendly policies, 30 vacation days |
Eligibility/Qualification
- A completed university degree (Master or equivalent) in:
- Computer Science
- Data Science
- Applied Mathematics
- Physics
- Materials Science
- Related fields
- Experience in:
- Computer Vision
- Deep Learning
- Signal Processing
- Familiarity with microscopy data (preferred but not required)
- Proficiency in Python and experience with ML/DL frameworks such as PyTorch or TensorFlow
- Strong analytical and communication skills
Description
This PhD project is methodologically independent, allowing contributions to collaborative efforts in data science, imaging, and materials research. Responsibilities include:
- Developing self-supervised learning frameworks
- Training and evaluating segmentation models
- Applying generative models to augment microscopy datasets
- Investigating domain adaptation techniques
- Collaborating with experimental partners to validate methods
- Disseminating findings through publications and conferences
How to Apply
Interested candidates should submit the following:
- CV
- University degree certificates
- Grade transcripts
- Two references/letters of recommendation
- A motivation letter highlighting relevant experience
Note: Applications cannot be accepted via email.
Last Date to Apply
Applications will be accepted until the position is filled. It is encouraged to apply as soon as possible.
