PhD student / Research Associate / Postdoc (m/f/d) in the field of Computational/Deep Learning in Neurooncology
Founded in 1607, Justus Liebig University Giessen (JLU) is a research university rich in tradition. Inspired by curiosity about the unknown, we enable around 26,500 students and 5,700 employees to advance science for society. Join us in breaking new ground and writing success stories - your own and those of our university.
Support us as of now as a
PhD student / Research Associate / Postdoc (m/f/d)
in the field of Computational/Deep Learning in Neurooncology
The position is part of the externally funded Junior Research Group "AI-RON - AI-assisted morphomolecular Precision Medicine in Neurooncology", headed by Dr. rer. nat. Daniel Amsel at the Institute of Neuropathology at the Faculty of Medicine (Director: Prof. Dr. med. Till Acker) and is limited until 30.09.2026 . The salary is in accordance with the collective labour agreement of the State of Hessen (E 13 TV-H: 75% PhD student, 100% Postdoc).
Your tasks at a glance
Classification of malignant tumors and identification of tumor vulnerabilities is a key step in defining therapeutic vulnerabilities and combat cancer. Merging molecular high dimensional data and histopathology with advanced medical (bio)informatics provides novel opportunities to develop effective precision oncology approaches. Our primary goal is to understand how somatic epi/genetic changes that arise during tumor progression correlate with (or induce) distinct histological phenotypes, specific alterations in signalling pathways and unique targetable cancer cell vulnerabilities. The proposed projects will aim to develop and employ machine/deep-learning-based tools for automated detection/quantification of histological tumor characteristics and their correlation with epi/genetic changes and signaling pathways by using genome-wide DNA methylation and NGS data from cancer patients and automated microscopy image analyses of histopathological samples including spatial transcriptomics.
Your qualifications and competences
- enthusiastic and dynamic applicants with a Master’S or PhD degree in Bioinformatics, Medical Informatics or a related discipline
- strong expertise in Python (PyTorch) or R
- expertise in UNIX/Linux (Ubuntu) and GPU Server systems
- experience in Docker and Machine Learning / Deep Learning
- Preference will be given to applicants with previous experience in one or more of the following areas: image analysis of histological data (e.g. with QuPath), analysis of Methylation EPIC/930k array, gene expression array, DNA- and RNA-seq, integrative quantitative and post-quantitative analyses from multiple omics datasets
Our offer to you
- An open, supportive, dynamic and motivating academic environment with excellent training opportunities including the PhD graduate program of the International Giessen Graduate school for Life Sciences (GGL http://www.uni-giessen.de/cms/fbz/zentren/ggl)
- Cutting Edge Hardware (Servers with 4x A100 80GB and Grace-Hopper Systems)
- A varied job with flexible working hours
- Free use of local public transport (LandesTicket Hessen)
- More than 100 training seminars, workshops and e-learning opportunities per year for personal development, as well as a wide range of health and sports activities
- Remuneration according to TV-H, company pension scheme, child allowance and special payments
- Good compatibility of family and career (certificate "audit familiengerechte hochschule")
If you have any further questions, please do not hesitate to contact Dr. Daniel Amsel by e-mail (Daniel.Amsel@patho.med.uni-giessen.de), see also www.ukgm.de/neuropathology, www.miracum.org.
JLU aims to employ more women in academic research. We therefore particularly encourage female candidates to apply. JLU is regarded as a family-friendly university. Applicants with children are very welcome. Applications from disabled people of equal aptitude will be given preference.
You want to break new ground with us?
Apply via our online form by September 3rd, 2024, indicating reference number 534/11. We look forward to receiving your application.