Das Universitätsklinikum Schleswig-Holstein bietet eine hervorragende Chance für ambitionierte Nachwuchswissenschaftler im Bereich der akuten myeloischen Leukämie. Im Rahmen eines innovativen Forschungsprojekts zur bioinformatischen Subtypen-Identifikation arbeiten Sie an der Schnittstelle von Machine Learning und Medizin. Sie analysieren umfangreiche Transkriptomik-Datensätze und entwickeln quelloffene Software-Tools für die Diagnostik. Die Position ermöglicht eine qualifizierte Promotion in einem hochdynamischen, interdisziplinären Umfeld. Neben moderner IT-Infrastruktur profitieren Sie von attraktiven Tarifleistungen im öffentlichen Dienst.
The Department of Internal Medicine II at the University Medical Center Schleswig-Holstein (UKSH) invites applications for a doctoral research position within the newly established LUCID-AML project. Funded by the Deutsche José Carreras Leukämie-Stiftung and connected with the DFG-funded Clinical Research Unit CATCH-ALL, this research initiative aims to decode developmental and prognostic subtypes of acute myeloid leukemia (AML) across different age groups through advanced machine learning and transcriptomics.
AML incidence increases steeply with age while survival rates decline. By integrating over 8,000 bulk AML gene-expression profiles with single-cell data, clinical outcomes, and drug-response profiles, LUCID-AML will establish robust, reproducible subtypes and provide transparent open-source diagnostic tools for research and clinical use.
Join Our Team
The position is available immediately. The initial appointment is granted for three years, with potential options for extension.
What We Offer
- Remuneration according to the German TV-L E13 pay scale (65% position, 25 hours per week) with an opportunity to earn a doctoral degree (PhD)
- Access to comprehensive AML transcriptomic datasets, annotated with extensive molecular and clinical parameters
- Collaborative environment with computational biologists, hematologists, and clinician-scientists on a national and international level
- Advanced training in bulk and single-cell transcriptomics, R package development, and machine learning leveraging high-performance computing infrastructure
- UKSH corporate benefits and institutional perks
Your Responsibilities
- Harmonize multi-cohort RNA-seq datasets and create automated workflows for quality control, batch correction, feature selection, and subtype discovery
- Develop calibrated machine learning models to transfer subtype classifications across cohorts and analytical platforms
- Evaluate subtype definitions against molecular genetic findings, WHO/ICC/ELN standards, and patient outcomes
- Map AML subtypes to hematopoietic development frameworks and validate bulk findings with single-cell transcriptomics
- Package optimized methods into open-source R tools and contribute to scientific publications and project reporting
Your Qualifications
- Master's degree (M.Sc. or equivalent) in bioinformatics, computational biology, data science, systems biology, or related quantitative life science disciplines
- Strong skills in R or Python, backed by a solid background in statistics or machine learning (willingness to work predominantly in R is essential)
- Prior experience with transcriptomics, cancer genomics, clustering, or R package creation is advantageous
- Highly motivated, collaborative mindset with independent problem-solving skills and strong dedication to medical research