Summary
The Scientific Computing Center (SCC) at the Karlsruhe Institute of Technology (KIT) is seeking a motivated PhD candidate to join the Collaborative Research Center (SFB) HyPERiON and the junior research group “Robust and Efficient AI.” This project focuses on leveraging artificial intelligence—specifically transformer-based deep learning models—to untangle overlapping and coupled signals in parallel Nuclear Magnetic Resonance (NMR) spectroscopy, a crucial tool for modern drug discovery.
PhD Position: AI-Driven Drug Screening at KIT, Germany
Designation
- PhD Position / Research Assistant (75% part-time)
Overview Table
| Feature | Details |
| Position Title | PhD Position (75% Part-Time) |
| Host Department | Scientific Computing Center (SCC) / SFB HyPERiON |
| Institution | Karlsruhe Institute of Technology (KIT) |
| Salary Grade | TV-L E13 (€43,200.00 – €46,400.00 gross/year) |
| Job Posting No. | 282/2026 |
| Start Date | As soon as possible (ASAP) |
| Contract Duration | Until June 30, 2030 |
| Location | Eggenstein-Leopoldshafen / Karlsruhe, Germany |
| Application Deadline | July 23, 2026 |
Research Area
- Artificial Intelligence & Deep Learning: Transformer architectures, self-supervised pre-training, masked sequence modeling, and transfer learning.
- Biophysics & Scientific Computing: Nuclear Magnetic Resonance (NMR) spectroscopy signal processing for drug screening.
- High-Performance Computing (HPC): GPU-accelerated computing and scalable AI for large scientific datasets.
Location
- Eggenstein-Leopoldshafen and Karlsruhe, Baden-Württemberg, Germany.
Eligibility / Qualification
- Degree: Master of Science (M.Sc.) in Computer Science, Physics, Mathematics, or an equivalent discipline.
- Programming: Very good programming and software development skills, preferably in Python.
- Experience: Prior experience with deep learning model development/training OR nuclear magnetic resonance (NMR) methods.
Job Description
As a PhD researcher in this project, your core duties will include:
- Neural Network Development: Developing transformer-based neural networks tailored to process complex NMR spectra.
- Experimental Data & Research Stay: Generating datasets from existing CRC experiments and conducting your own experiments during a research stay at KIT’s Institute of Microstructure Technology (IMT).
- Model Training: Applying self-supervised pre-training (masked sequence modeling) followed by task-specific fine-tuning to untangle coupled NMR spectra.
- Physical Interpretability: Analyzing how effectively the developed AI models capture the underlying physical principles of NMR.
- Collaborative Research: Actively participating in SFB HyPERiON network activities, workshops, and interdisciplinary collaborations.
How to Apply
Applications should be submitted directly online through the KIT Job Portal Position Page.
Contact Details
- Line Management Contact: Dr. Charlotte Debus (charlotte.debus@kit.edu)
- Human Resources (PSE) Enquiries: Dominik Meschar (dominik.meschar@kit.edu | +49 721 608-25029)
Last Date to Apply
- July 23, 2026
