Research Associate (m/f/d) at Else Kröner Fresenius-Zentrum

Research Associate (m/f/d) at Else Kröner Fresenius-Zentrum

Anticipated Start Date: 01.11.2026

Application End Date: 11.10.2026

Temporary Employment: 24 months

Working Hours: Full and Part Time

Requisition ID: 2139

The University Hospital Carl Gustav Carus and the Faculty of Medicine together form University Medicine Dresden. It is committed to excellence in high-performance medicine, medical research and teaching as well as healthcare services for patients in the entire region.

Together for the cutting-edge medicine of tomorrow - become part of University Medicine Dresden

Research Associate (m/f/d) in the field of Efficient AI for Computational Pathology

at Else Kröner Fresenius-Zentrum

The position is available from November 1st, 2026, full-time and initially limited to 24 months, with the possibility of extension and longer-term collaboration. Remuneration is based on the provisions of the collective agreement for the public service of the German federal states (TV-L) and, subject to the relevant personal qualifications, may be classified at pay grade E13 .

The position is part of the research project “MedSecureAI – Certifiable Chip Platform for Secure AI Accelerator Integration in Image-Based Medical Detection.” The project is part of the SEMECO Future Cluster and combines research in medical AI, digital pathology, edge computing, and trustworthy hardware platforms.

The aim of the project is to develop and validate a modular, secure, and resource-efficient platform for AI-assisted medical image analysis. In the medical use case, AI models for digital pathology will initially be trained on high-performance GPU infrastructure and subsequently optimized for local execution at the microscope on resource-constrained edge hardware. A demonstrator will enable direct and explainable analysis of histological image data at the microscope.

The successful candidate will work in an interdisciplinary and international research environment in the research group of Marco Gustav, PhD, at the Else Kröner Fresenius Center for Digital Health at TU Dresden, and will collaborate closely with academic institutions and industry partners. The group develops practical AI solutions for challenges in clinical care. A particular focus is on efficient, locally deployable AI methods for pathology and their clinically relevant application.

Your responsibilities:

Identification, selection, and preparation of suitable histological image datasets for the development and validation of AI models

Development, implementation, and optimization of deep-learning models for histological image data using PyTorch

Design and implementation of efficient data and training pipelines, including data loading, preprocessing, and training

Optimization and porting of the developed models to the target hardware, particularly with regard to computational and memory efficiency as well as inference speed, taking edge AI approaches into account and working closely with the technical project partners

Development and validation of a demonstrator for local AI-assisted analysis of histological image data at the microscope

Scientific analysis, documentation, and publication of research results in peer-reviewed journals, as well as presentation at scientific conferences

Your profile:

Completed university degree in Computer Science, Artificial Intelligence, Engineering, Data Science, or a related field

Strong programming skills in Python and PyTorch, as well as very good knowledge of deep learning