Post-Doc in Artificial Intelligence for Breast Cancer Image Analysis (m/f/d)

Robert Bosch Krankenhaus GmbH; Deutschland; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A leading research institute in Germany is seeking a PhD-level candidate for a unique opportunity in computational pathology and oncology. The role focuses on analyzing histopathology datasets and developing machine learning models to enhance cancer research. Candidates should possess expertise in AI, data science, and image analysis, with a strong publication record. The position offers collaboration with national and international partners and access to state-of-the-art resources.

Leistungen

Dynamic collaborative environment

Access to state-of-the-art computational resources

Opportunities to publish in top-tier journals

Engage in international research collaborations

Qualifikationen

• PhD or equivalent in bioinformatics required.
• Expertise in AI-driven image analysis and data science.

Aufgaben

• Analyze large-scale histopathology datasets from breast cancer tissue.
• Develop machine learning models for image-based biomarker discovery.

Kenntnisse

Artificial Intelligence

Data Science

Communication

Interdisciplinary Research

Ausbildung

PhD in Bioinformatics

Tools

Python

PyTorch

TensorFlow

Unix/Linux Systems

Jobbeschreibung

Company Description

The Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart (Director: Prof. Dr. M. Schwab) is operated by the Robert Bosch Foundation. Since its foundation in 1973, the institute has emerged as Germany's largest and most renowned scientific research unit in the field of clinical pharmacology. Main research activities concentrate on the influence of genetic, epigenetic, metabolic and non-genetic factors on variability of drug efficacy and their contributions to disease development.

Job Description

The project involves analyzing large-scale histopathology datasets from breast cancer tissue and developing machine learning models for image-based biomarker discovery. This position offers a unique opportunity to work at the intersection of computational pathology, artificial intelligence (AI), and oncology, contributing to clinical cancer research through advanced computer vision applied to exclusive image datasets.

We aim to use cancer cell morphology and nuclear features in tissue slides to predict breast cancer (molecular) subtypes, prognosis and treatment response. This work is carried out in close collaboration with our outstanding national and international biomedical partners.

Qualifications

The ideal candidate will have a PhD or equivalent in bioinformatics, with expertise in various methods of artificial intelligence and data science. A strong background in AI-driven image analysis, proficiency in Python programming including deep learning frameworks such as PyTorch and TensorFlow, and a solid publication record are required. Additional desirable qualifications include:

• Experience in working with whole-slide images in histopathology
• Proficiency in working with Unix/Linux Systems
• Excellent communication skills and fluency in English
• Interest in interdisciplinary research
• Knowledge of cancer biology

Additional Information

We offer:

• A dynamic and collaborative environment focused on the clinical application of new biological insights
• Access to state-of-the-art computational resources and unique, high-quality histopathology datasets
• Opportunities to publish in top-tier journals and engage in international research collaborations
• A highly motivated interdisciplinary team

The position is available immediately.

Contact:

Dr. Florian Büttner
Phone: [phone]

The Dr. Margarete Fischer-Bosch-Institute for Clinical Pharmacology seeks to raise the number of women in research and teaching and therefore urges qualified women academics to apply for these positions. Equally qualified applicants with disabilities will be given preference.

Please submit your application including motivation letter, CV, certificates and contact details of two academic references.

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