Post-Doc in Artificial Intelligence for Breast Cancer Image Analysis mfd
Post-Doc in Artificial Intelligence for Breast Cancer Image Analysis mfd
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Job Description
The project involves analyzing largescale histopathology datasets from breast cancer tissue and developing machine learning models for imagebased 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 AIdriven 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 wholeslide 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 stateoftheart computational resources and unique highquality histopathology datasets
- Opportunities to publish in toptier journals and engage in international research collaborations
- A highly motivated interdisciplinary team
The position is available immediately.
Contact:
Dr. Florian Bttner
Phone: 0711/
The Dr. Margarete FischerBoschInstitute 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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Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
