Forschung und Entwicklung

Wissenschaftliche MA/Promotion

01.10.2025

03.09.2025 (noch 10 Tage)

Deutschland

Sachsen-Anhalt

Alle

The Network Analysis and Modelling uses machine learning to investigate how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. We are seeking a highly motivated PhD student to join a DFG-funded international project that investigates plant - microbiome interactions through large-scale metabolomics and other - omics platforms.

Your tasks:
+ Process and curate LC-MS/MS data for high-quality feature extraction
+ Design and train machine-learning models for mass spectrometry and chemometric data
+ Integrate multi-omic data including genomics and transcriptomics
+ Present findings at conferences and publish in peer-reviewed journals

Your qualifications and skills:
+ You hold a Master’s degree (or equivalent) in Bioinformatics, Data Science, Systems Biology or other relevant fields.
+ You have programming experience in Python and/or R (e.g. scikit-learn, PyTorch, TensorFlow).
+ You have knowledge of omics technologies, ideally mass spectrometry and chemometrics.
+ You also have knowledge of basic molecular biology/biochemistry.

We offer you:
+ a world-class research environment with state-of-the-art omics, high-performance computing and imaging facilities.
+ a collegial, international atmosphere and flexible, family-friendly working hours
+ a structured PhD programme with extensive training and career-development opportunities.
+ a 3-year project-based position, starting on 01.10.2025
+ a gross salary up to 65% E13 TV-L.

Your application:

We are looking forward to receive your complete online-application (letter of motivation, CV, certificates) as one single pdf-document until 03.09.2025.Please indicate the reference number 52/07/25 in your correspondence. Foreign qualifications must carry out an equivalence test in Germany, which is subject to a fee. This must be presented in the event of a later hiring: https://zab.kmk.org/en/statement-comparability