Doctoral Researcher (Ph.D. candidate) (m/f/d) Computational Immunology

University of Regensburg; Regensburg; Hybrid; EUR 42.000 - 60.000; Vollzeit

Benefits dieser Stelle

Mentoring

Training

Conference funding

Flexible hours

Partial remote work

Open-source contributions

Zusammenfassung

The University of Regensburg invites applications for a doctoral position with a focus on metabolic RNA labeling data and single-cell technologies. The group works at the interface of data science and biology and collaborates with labs worldwide.

You will develop statistical methods, apply tools to data from collaborators, contribute to open-source software, and publish results at international conferences while pursuing a doctoral degree with structured supervision.

Qualifikationen

• Master’s or equivalent in a quantitative field is required.
• Solid knowledge of statistical methods and practical programming experience (R, Python, or Java).
• Good English communication and willingness to conduct interdisciplinary research.

Aufgaben

• Develop and implement statistical methods for single-cell RNA sequencing data (scSLAM-seq).
• Apply developed tools to data from collaboration partners.
• Maintain and extend GRAND-SLAM / grandR software packages.

Kenntnisse

Statistical methods

Programming in R/Python/Java

English communication

Ausbildung

Master's degree or equivalent in bioinformatics / stats / CS / data science / physics

Tools

GRAND-SLAM

grandR

Git / reproducible workflows

Jobbeschreibung

Our research group is internationally leading in developing computational and statistical methods for metabolic RNA labeling data, with a strong focus on single-cell technologies (scSLAM-seq). We combine rigorous statistical modeling and high-performance software development (e.g., GRAND-SLAM, grandR) with cutting-edge applications in RNA kinetics, virology, and immunology. Our interdisciplinary team works at the interface of data science and high-throughput molecular biology, maintaining close collaborations with experimental labs worldwide to decode the temporal dynamics of gene regulation.

We Offer

• A dedicated DFG-funded research project focused on novel method development with a strong potential for high-ranking publications
• Close integration into an internationally leading research group specializing in computational methods for metabolic RNA labeling and single-cell genomics
• Direct contribution of your methodological work to established open-source tools (GRAND-SLAM/grandR) used by researchers worldwide
• Structured, close mentoring and comprehensive training in theoretical and practical bioinformatics, statistical modeling, and interdisciplinary communication
• Full financial support for presenting your research at national and international conferences
• Flexible working arrangements (including flexible hours and partial remote work) to support diverse life situations

Your Tasks

• Development and implementation of statistical methods and algorithms for single-cell RNA sequencing data, with a special focus on metabolic RNA labeling (scSLAM-seq)
• Application of developed computational tools to data generated by our network of collaboration partners
• Contributing to the maintenance and extension of our software packages (e.g., GRAND-SLAM / grandR)
• Publication of research results in peer-reviewed scientific journals and presentations at international conferences
• Pursuit of a doctoral degree (Dr. rer. nat. / PhD) supported by structured supervision

Your Profile

• A university degree (Master’s or equivalent) in bioinformatics, statistics, computer science, data science, physics, or a related quantitative field is required
• Solid theoretical knowledge of statistical methods and practical programming experience (preferably in R, Python, or Java) are required
• Good communication skills in English, a high degree of intrinsic motivation, and the willingness to conduct interdisciplinary research bridging data science and biology are expected

Desirable Qualifications

• Prior experience in working with biological high-throughput molecular data (e.g., bulk or single-cell RNA-seq)
• Familiarity with software development tools (e.g., Git, reproducible workflows)
• Basic knowledge of RNA biology and immunology

We support women and strongly encourage them to apply. As a certified family-friendly university, we support our employees in balancing family and career.

Applicants with a disability as described in SGB IX (2 Abs. 2, 3) will be preferred in case of equal qualifications.