Researcher and Software Developer on Graph Modeling and Machine Learning

„Research for a life without cancer“ is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention strategies. We develop new methods with which tumors can be diagnosed more precisely and cancer patients can be treated more successfully. Every contribution counts – whether in research, administration or infrastructure. This is what makes our daily work so meaningful and exciting.

The Clinical Cooperation Unit "Applied Tumor Immunity" at the German Cancer Research Center and the National Center for Tumor Diseases Heidelberg is seeking a highly motivated Researcher and Software Developer on Graph Modeling and Machine Learning.

Reference number: 2024-0279

The National Center for Tumor Diseases (NCT) is a long-term cooperation between the German Cancer Research Center (DKFZ), excellent partners in university medicine and other research institutions at six locations in Germany. The goal of the NCT is to bring promising results from cancer research into clinical application quickly and safely. This approach is intended to provide cancer patients with nationwide access to innovative treatment options.

One of the six NCT sites is the NCT Heidelberg, which is a collaboration between the DKFZ, Heidelberg University Hospital (UKHD), Heidelberg University and Thoraxklinik Heidelberg.

The successful applicant will join the dynamic multidisciplinary team – comprising wet-lab scientists, physicians and computational scientists – of the Applied Tumor Immunity Clinical Cooperation Unit headed by Prof. Dr. Dirk Jäger at DKFZ and NCT Heidelberg. You will have the opportunity to work on cutting-edge projects and the chance to make a significant impact on data-driven biomedicine.

• Design, develop and maintain graph database solutions using Python and graph database technologies (e.g. Neo4J, ArangoDB, OrientDB)
• 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
• Design and develop graph machine / deep learning algorithms to solve challenging problems in biomedicine
• Stay current with the latest developments in graph machine learning and develop / integrate new techniques into biomedically-relevant research projects
• Conduct testing and provide thorough documentation

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
• 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
• 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:

• Knowledge of graph database concepts and technologies (e.g. Neo4J, ArangoDB, OrientDB)
• 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)

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.

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