Background
Most food allergies (FA) start in early childhood and lay the tracks towards an allergy career throughout life. Therefore, early identification of children at risk and prevention of FA are highly relevant needs. We aim to investigate both, early causes and natural history of FA, enabling us to put forward a meaningful prediction model of FA. To this end, we have established a consortium, NAMIBIO, that aims to capitalize on some of the largest and best-characterized German birth cohort studies, providing comprehensive longitudinal, multi-modal datasets including clinical data, demographics, lifestyle and psychosocial factors. DNA methylation sequencing and clinical data will be analyzed to develop a machine learning strategy for identification of an FA bio-marker set. Project start of NAMIBIO is presumably July 2021.
Your area of responsibility The Digital Health Centre of Roland Eils provides a dynamic, collaborative, challenging and rewarding research environment. The department offers access to cutting edge computational resources including GPUs / DGXs, FPGAs and HPC. The advertised position will be embedded in the research group of Computational Medicine headed by Dr. Naveed Ishaque, with very close collaboration with PIs of the consortium, Prof Irina Lehmann and Prof Roland Eils. Your responsibilities in the context of the NAMIBIO consortium are to combine clinic data with genome-wide epigenetic data of cord blood in order to develop a machine learning strategy to unravel food allergy specific methylation biomarkers.
You will be expected employ appropriate computation approaches to:
· process and evaluate the quality of whole genome DNA methylation sequencing data of cord blood samples
· analyzing DNA methylation sequencing data, including differential methylation calling
· implementing machine learning models to identify food allergy related DNA methylation signatures
· investigate the role of epigenetic mechanisms in food allergy development with the goal of defining epigenetic biomarkers
· Scientific staff is given sufficient time to carry out their own scientific work in accordance with their employment relationship.
Your profile
· a PhD in bioinformatics, computer science, physics, biotechnology, immunology or similar
· ambitious, self-driven, a thirst for answering scientific questions and aim to build a scientific career in biomedical data science research
· good track record of publishing peer-reviewed articles
· excellent programming proficiency especially in Python, R, Julia, bash or similar
· experience with DNA methylation data processing and analysis
· sound statistical knowledge of experimental design including confounders / batch effects and power analysis
· experience with linux based HPC environments
· fluent in English (written and spoken)
Qualifications – desirable
· prior research experience in epigenomics of allergy or immunology
· experience with machine learning methods, including principles such as over fitting, feature importance, nested cross validation
· experience with whole genome DNA methylation sequencing data (e. g. using tools such as bismark, dss, bissnp, rnbeads)
· experience with genome-epigenome interactions such as DNA methylation QTLs (e. g. using Matrix eQTL)
· experience working with data of Epigenomics consortia and projects (e. g. ENCODE, ROADMAP, Blueprint, 4DN, genehancer)
Ref. no.: DM.124.21b
Email:
Start: immediately
Length of employment: 3 years
Working time: 39 hrs./week
Pay scale: E13 acc. to collective agreement TVöD VKA-K
Please send all application documents (e. g. cover letter, curriculum vitae, diplomas, certificates etc.) by the application deadline date 13.07.2021 and quoting the reference number DM.124.21b to the following address: