PhD position (all genders) - deep learning for biomedical image analysis at UKE Universittsklinikum HamburgEppendorf
Hamburg, Hamburg, Germany -
Full Time


Start Date

Immediate

Expiry Date

26 Mar, 25

Salary

0.0

Posted On

17 Feb, 25

Experience

0 year(s) or above

Remote Job

No

Telecommute

No

Sponsor Visa

No

Skills

Good communication skills

Industry

Hospital/Health Care

Description

BETTER TOGETHER. FOR LIFE.

At the University Medical Center Hamburg-Eppendorf (UKE), we are committed to excellence in research, education, and comprehensive healthcare across our clinics. Every day, our team of approximately 15,300 dedicated employees works to make the world a healthier place. Our goal is to be one of the leading university hospitals while also being the best employer in our industry.
At UKE, we believe that meaningful and successful work should align with our employees’ personal needs and individual lifestyles. Just as diverse as these needs are, so too is the variety of personalized solutions we offer.

Responsibilities

MAIN TASKS

The Group of Computational Pathology is part of the Institute of Medical Systems Bioinformatics (IMSB), and is a young team working on biomedical image analysis, in particular microscopy images. It is led by Prof. Dr. Marina Zimmermann. The group is also a member of bAIome, the Center for Biomedical AI (www.baiome.org), a platform at UKE where AI and clinical expertise team up with the aim of bringing next-generation software solutions into the clinic.
We currently offer a position in biomedical image analysis using deep learning and are looking for highly motivated students and graduates to complement our lively and enthusiastic team of scientists starting on 1st April 2025. Embedded in our group, the successful candidate will develop state-of-the-art deep learning-based algorithms and apply them to a wide range of medical problems.

YOUR TASKS:

  • Design of novel and application of existing cutting-edge computer vision algorithms (e.g. CNNs, Vision Transformers, Foundation Models, Explainable AI) for the automatic classification and segmentation of microscopy data (supervised, weakly supervised and unsupervised), and the subsequent prediction of disease progression in direct collaboration with clinical researchers.
    This position is, due to third-party funding, temporary for 3 years (80% of regular working hours).
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