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PhD Student – Bioinformatics / Computational Biology (m/f/d)

Salary

€57.708k

Min Experience

0 years

Location

Heidelberg, Baden-Württemberg

JobType

full-time

About the job

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About the role

Join our exciting mission to decode leukemic dormancy and plasticity in AML! The research group of Dr. Junyan Lu at the Medical Faculty of Heidelberg University ( https://lu-group-ukhd.github.io/ ) , in collaboration with Prof. Dr. Irmela Jeremias, invites applications for a PhD student in Bioinformatics / Computational Biology as part of the CRC1709-funded project "Plastic growth behavior of patients' AML in vivo: Releasing dormant cells from their protective niche." We are an interdisciplinary and collaborative research team at the forefront of multi-omics data integration, cancer systems biology, and single-cell analysis, with strong links to clinical and translational oncology. Our work is based in the dynamic research environment of Heidelberg University, one of Europe's leading biomedical institutions. This position focuses on unraveling the regulatory networks underlying dormancy and therapeutic resistance in acute myeloid leukemia (AML). Using advanced computational tools and single-cell multi-omics data (CITE-seq, proteomics, transcriptomics, methylation, metabolomics), we aim to: 1) Identify surface molecules specific to dormant leukemic stem cells; 2) Model AML plasticity at the single-cell level; 3) Propose candidate therapeutic targets for in vivo validation. The PhD student will be embedded in a collaborative and supportive team, working closely with a wet-lab PhD student from Prof. Dr. Jeremias' team.

About the company

The Heidelberg University School of Medicine (also known as the Medical Faculty of Heidelberg, German: Medizinische Fakultät Heidelberg) is one of twelve schools at Heidelberg University. It was one of the four original faculties of the university in 1386. Today, it encompasses 22 institutes and maintains a close relationship to the University Hospital Heidelberg.

Skills

bioinformatics
r
python
machine learning