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Job Description

Specific tasks:
• Design and develop graph machine/deep learning algorithms to solve challenging problems in biomedicine
• Design, develop, and maintain graph database solutions using Python and graph database technologies (e.g. Neo4J, ArangoDB, OrientDB)
• Create and optimize graph data models to represent complex relationships and entities
• Implement data import/export processes and ETL pipelines for graph data and write efficient and performant code to query and manipulate graph data
• Collaborate with our multidisciplinary team to apply graph-based models to biomedical datasets as well as implement and optimize algorithms for efficient computation on large-scale graphs
• Conduct experiments, analyze results, and interpret findings to drive impactful research contributions
• Stay current with the latest developments in graph machine learning, and develop/integrate new techniques into biomedically-relevant research projects

General Tasks
• Design, develop, and... maintain high quality research software tools
• Perform continuous feature enhancements and improvements
• Conduct testing and thorough documentation to enhance tool reliability and reusability
• Publish findings in academic journals and present work at conferences and seminars
• Ensure compliance with ethical and safety standards


Mandatory qualifications:
• A master's or PhD degree in computer science, data science, computational biology, electrical engineering, physics, mathematics, statistics, or related field with a focus on machine learning and graph theory; applicants with life sciences background but proven programming and computational experience are encouraged to apply
• Strong foundation in machine learning, deep learning, and graph theory
• Strong knowledge of graph database concepts and technologies (e.g. Neo4J, ArangoDB, OrientDB)
• Experience with graph neural networks and related libraries, tools, and frameworks (e.g. PyTorch Geometric, Deep Graph Library, GraphSAGE)
• Proficiency in programming languages such as Python, TensorFlow, PyTorch, or similar tools for machine/deep learning development
• Proficiency in querying graph databases using query languages (e.g. Cypher).
• Proven track record of research publications in journals and conferences related to the position
• Strong problem-solving skills, excellent written and communication skills, with the ability to work independently but also within our multidisciplinary research environment
• Ability to collaborate with life scientists, clinicians, and researchers from diverse disciplines to address research questions at the intersection of graph machine learning and biomedicine

Optional Qualifications
• Experience applying graph machine learning techniques to analyze and interpret omics (e.g. genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics) and clinical data
• Experience in managing diverse data types, integrating multi-omics datasets, and dealing with data heterogeneity
• Proficiency in data preprocessing, normalization, and feature extraction techniques specific to biomedical applications
• Experience in constructing, visualizing, and analyzing biological networks (e.g. protein-protein interaction networks, gene regulatory networks, metabolic pathways, disease networks) using graph representation techniques
• Experience in the development of graph learning models for personalized medicine applications, e.g. predicting patient outcomes, treatment responses, and disease trajectories based on individualized molecular profiles
• Additional experience in other programming languages and database systems

Your CV and cover letter outlining your research accomplishments and programming experience must be included in your application. Additionally, the contact information of 2-3 referees who can speak to your academic or professional qualifications must be included in your application.

We Offer
• Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level
• Remuneration according to TV-L incl. occupational pension plan and capital-forming payments
• 30 days of vacation per year
• Flexible working hours
• Possibility of mobile work and part-time work
• Family-friendly working environment, e.g. parent-child room, advisory services caring for elderly relatives
• Sustainable travel to work: subsidized Germany job ticket
• Unleash your full potential: targeted offers for your personal development to further develop your talents
• Our Corporate Health Management Program offers a holistic approach to your well-being

Important Notice

The DKFZ is subject to the regulations of the Infection Protection Act (IfSG). As a consequence, only persons who present proof of immunity against measles may work at the DKFZ.

Earliest Possible Start Date: as soon as possible

Duration: The position is initially limited to 2 years with the possibility of prolongation.

Application Deadline: 04.06.2024


Marion Drechsel

Phone +49 6221 56-37498

Please note that we do not accept applications submitted via email


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