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Maximilian Tschochohei

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1 week ago

PhD Position in Explainable AI for Gynecological Oncology (VIOLET Project) Technical University of Munich in Germany

I am recruiting a fully funded PhD student for the VIOLET Project in explainable AI for gynecological oncology at the Technical University of Munich.

Technical University of Munich

Germany

email-of-the@publisher.com

Date not provided

Keywords

Computer Science
Information Technology
Health Science
Open Science
Personalized Medicine
Python Programming
Database Management
Clinical Informatics
Explainable Ai
Gynecologic Oncology
Knowledge Representation
High Performance Computing
Clinical Documentation
Large Language Models
Machine learning

Description

The VIOLET Project at the Technical University of Munich (TUM) is offering a fully funded PhD position focused on developing explainable and interoperable AI systems for gynecological oncology. This pioneering research initiative aims to create a methodological framework that ensures AI in medicine is transparent, trustworthy, and clinically relevant. As a PhD candidate, you will contribute to the architecture and technical design of the VIOLET research prototype, design and develop AI systems for guideline-compliant therapy recommendations using large language models (LLM), retrieval-augmented generation (RAG), and graph paradigms, and translate clinical guidelines into formal ontologies and knowledge graphs. You will also work on modules for interoperability with the Medical Informatics Initiative (MII) and FHIR standards, collaborating closely with clinicians and data scientists to ensure the developed systems meet real-world clinical needs and are validated accordingly. The position requires publishing research results in peer-reviewed journals and presenting at international conferences. Applicants should have an excellent Master’s degree in Computer Science, Medical Informatics, or a related field, with a final grade of at least magna cum laude (or equivalent), and documented expertise in machine learning model development, high-performance computing, data management, and software architecture. Strong Python programming skills and familiarity with machine learning frameworks such as PyTorch are essential, as is a deep interest in knowledge graphs, LLMs, and open science. Proficiency in English and at least B2-level German is required. The successful candidate will benefit from a structured doctoral pathway, access to unique large-scale medical datasets, high-performance computing infrastructure (including NVIDIA B300 GPUs), and funding for publications, international conferences, and research mobility. Additional benefits include EGYM Wellpass, corporate benefits, JobBike Bavaria, VBL pension plan, and free use of the Munich City Library branch located in the building. The position is based in the heart of Munich at Max-Weber-Platz, with excellent public transport connections. Applications are accepted until the position is filled, and candidates with disabilities will be given preference in cases of equal suitability. For further information or to apply, contact Maximilian Tschochohei, PhD, at the provided email address or visit the TUM job portal.

Funding

Available

How to apply

Submit your application via the TUM job portal at the provided link. Applications will be considered until the positions are filled. For questions, contact Maximilian Tschochohei, PhD, at the provided email address. Ensure you review the data protection information before applying.

Requirements

Applicants must have an excellent Master’s degree in Computer Science, Medical Informatics, or a related field, with a final grade of at least magna cum laude (or equivalent) demonstrating research excellence. Documented expertise in developing and training machine learning models (ideally with a focus on large language models), high-performance computing, data management, and software architecture is required. Strong Python programming skills and familiarity with machine learning frameworks such as PyTorch are essential. Candidates should have a deep interest in knowledge graphs, LLMs, and open science, as well as a passion for tackling complex scientific challenges and a meticulous approach to research and validation. Strong analytical and problem-solving skills are necessary, with a keen interest in applying AI to personalized medicine. Excellent communication skills in English and proficiency in German (at least B2 level) are required.

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