PhD Position—Machine Learning & Genomics in Antimicrobial Resistance Diagnostics (f/m/d)

DZIF

Tübingen

Hybrid

EUR 36.000 - 60.000

Teilzeit

Vor 5 Tagen
Sei unter den ersten Bewerbenden
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Benefits dieser Stelle

Parking space sharing
Canteen discounts
Job bike
In-house academy
Diverse training opportunities

Zusammenfassung

The DZIF Institute for Medical Microbiology, Medical Virology and Hygiene in Tübingen is offering a part-time research position (65%) as part of the AG Sattler group. You will develop and evaluate machine-learning models to detect resistance plasmids from MALDI-TOF spectra and perform genomic analyses of carbapenemase-producing Enterobacterales using short- and long-read sequencing.

You will join a new research group with close supervision, strong collaboration with CMFI and IGIM, and

Qualifikationen

  • Completed master's degree in bioinformatics, microbiology, or a comparable subject.
  • Confident programming in Python and experience working in a Linux environment with bioinformatics tools.
  • Practical experience with machine learning and/or analysis of whole-genome sequencing data.
  • Interest in bacterial genomics and clinical microbiology and willingness to combine computational work with experiments at the bench.
  • Independent and structured working style, team orientation and scientific rigour, ideally evidenced by a first publication.

Aufgaben

  • Development and clinical evaluation of machine-learning models for the detection of resistance plasmids directly from routine MALDI-TOF mass spectra.
  • Genomic analysis of carbapenemase-producing Enterobacterales and their mobile genetic elements, using short- and long-read whole genome sequencing.
  • Establishment and analysis of linked MALDI-TOF and genome datasets as the basis for generalisable predictive models.
  • Experimental validation of computational predictions through conjugation, plasmid curing and stability assays, supported by a dedicated technical assistant.
  • Contribution to national and international consortia (DZIF TTU HAI, ESGEM-AMR) and presentation of results at conferences and in peer-reviewed journals.

Kenntnisse

Python
Linux
ML/Genomics
Genomic analysis
Scientific rigor

Ausbildung

Master's degree in bioinformatics or microbiology

Tools

Bioinformatics tools

Jobbeschreibung

Institute for Medical Microbiology, Medical Virology and Hygiene, Reference number 7903

Part-time: 65 %
Limited: 30.11.2029*
Start of work: 01.12.2026
Application deadline: 20.10.2026
TV-L: generally E13

* extension possible

Tasks
  • Development and clinical evaluation of machine-learning models for the detection of resistance plasmids directly from routine MALDI-TOF mass spectra
  • Genomic analysis of carbapenemase-producing Enterobacterales and their mobile genetic elements, using short- and long-read whole genome sequencing
  • Establishment and analysis of linked MALDI-TOF and genome datasets as the basis for generalisable predictive models
  • Experimental validation of computational predictions through conjugation, plasmid curing and stability assays, supported by a dedicated technical assistant
  • Contribution to national and international consortia ( DZIF TTU HAI , ESGEM-AMR) and presentation of results at conferences and in peer-reviewed journals
Profile
  • Completed Master's degree in bioinformatics, microbiology, or a comparable subject
  • Confident programming in Python and experience working in a Linux environment with bioinformatics tools
  • Practical experience with machine learning and/or analysis of whole-genome sequencing data
  • Interest in bacterial genomics and clinical microbiology and willingness to combine computational work with experiments at the bench
  • Independent and structured working style, team orientation and scientific rigour, ideally evidenced by a first publication

You will join a newly established research group as one of its first members, with close supervision, technical support, and scope to shape your research environment, while benefiting from regular exchange with CMFI and participation in IGIM.

  • innovative university hospital
  • state-of-the-art technology
  • world-class international research
  • excellent career prospects
  • structured onboarding
  • in-house academy
  • diverse training opportunities

Internationality

  • cultural & generational diversity
  • support through language courses

Research

  • cutting-edge research at the highest level
  • support from PhD to professorship

Mobility & offers

  • good public transport connections
  • parking space sharing & ride-sharing service
  • discounts in the canteen & cafeteria
  • job bike & other corporate benefits
About us

The Faculty of Medicine is one of the four founding faculties of the Eberhard Karls University of Tübingen. With its non-clinical facilities as well as its research and teaching area corresponding to the organisational units of the University Hospital, it is one of the largest medical training and research institutions in Baden-Württemberg.

About the research group

Newly established research group at the interface of medical microbiology, bacterial genomics and machine learning (AG Sattler). We develop data-driven approaches to antimicrobial resistance diagnostics and investigate the genomic epidemiology of multidrug-resistant bacteria, combining computational analysis with experimental validation.

Reference number: 7903

We offer remuneration in accordance with TV-L (collective wage agreement for the Public Service of the German Federal States), severely handicapped persons with equal qualifications are given preferential consideration. Interview expenses are not covered. Please note the applicable vaccination regulations. The employer actively promotes equal opportunities and particularly encourages applications from women.

Continued employment will be sought whereever possible.

The data provided does not constitute a basis for an employment contract at the University Hospital of Tübingen. It is intended to serve as a rough guide for all interested parties. Remuneration is governed by the applicable collective bargaining agreements in conjunction with the corresponding pay scales.

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