Scientist (m/f/d)

at  Uniklinik RWTH Aachen

Job-Benachrichtigung per E-Mail
Stellenangebote
As a tertiary and quaternary level provider of medical care, the University Hospital of RWTH Aachen combines patient-oriented medicine and care, teaching and research at an international level. The entire spectrum of medical care is provided through 36 specialist departments, 33 institutes and six interdisciplinary units. The co-location of patient care, research and teaching in a central building offers the best conditions for intensive interdisciplinary exchange and close clinical and scientific networking. Highly qualified teams of doctors, nurses and scientists work collaboratively to optimise the healthcare provision of our local, regional, national and international patients. Around 9,000 employees ensure that our care delivery is patient-oriented medicine and surpasses the highest quality standards. With 1,400 beds, the university hospital treats around 50,000 inpatient and 200,000 outpatient cases per year.
The Institute of Pathology -LaBooratory of Nephropathology/AG Boor & AG Bülow- opend a new position for a

SCIENTIST (M/F/D) IN APPLIED AI IN DIGITAL PATHOLOGY

The full-time position (38,5 hours/week.) starts as soon as possible and is initially expected to be limited to 1 year with the possibility of prolongation until the completion of the doctoral degree.
Our group has a focus on developing novel methods for digital pathology, advanced image analysis and deep learning. Major research areas in our group are image segmentation, next-generation morphometry and pathomics data analysis, sustainable AI (or green/energy-efficient AI), as well as secure and explainable AI.
We are looking for a scientist (PhD student or postdoc) who can make innovative contributions to these research areas.
We are an interdisciplinary and international team of young AI researchers, biologists and clinician scientists working on medical applications at the Institute of Pathology. Our hardware equipment comprises several GPU (NVIDIA DGX A100) and CPU clusters, and we have a continuously growing collection of both preclinical data sets and large multi-center data sets of patient biopsies.

• Development, optimization, application, and clinical validation of deep learning algorithms in digital pathology
• Optimization of models and analyses for sustainability and safety
• Expanding our knowledge of pathology through applications of explainability and interpretability
• Techniques, as well as extensive feature analysis
• Evaluation, presentation (visualization), and interpretation of results
• Independent review of the literature
• Data management