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Our mission as research center: Discover personalized medical solutions for environmentally triggered diseases to promote a healthier society in a rapidly changing world.

The Institute of Translational Genomics (ITG) translates insights from genomics and multi-omics into mechanisms of complex disease development and progression. The objective of this PhD will be methodological development using machine learning approaches for the analysis of multi-omics data in order to improve type 2 diabetes (T2D) prognosis and prevention approaches, achieve better patient stratification and identify potential new drug targets.

The successful candidate will join a dynamic and international team of researchers and analysts in a collaboration funded through the EU Horizon2020 framework, which brings  together both private  and public researchers  to  achieve a paradigm shift  in T2D healthcare. The project will provide a unique opportunity to gain experience with diverse omics datasets as well as  a wide spectrum of translational genomics analyses.

We are looking for a highly motivated

PhD candidate - machine learning method development for multi-omics analysis with an application to type 2 diabetes (f/m/x) 101714

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Image Neuherberg near Munich
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Your mission

  • Development of new methods for the analysis of multi-omics data (genotyping data, proteins, metabolites, imaging traits) using machine learning approaches
  • Performing exploratory data analysis, quality control and statistical mining on large datasets containing T2D-related data
  • Developing new pipelines and software, quality control
  • Identification and validation of novel drug targets and drug repositioning opportunities
  • Development and validation of integrated prediction score combining metabolomics, proteomics, genetics and imaging measurements

Your qualifications

  • A Master’s degree in Computer Sciences or a related field
  • Practical experience with machine learning approaches
  • Familiarity with *NIX environments and the bash scripting language
  • Familiarity with high-performance computing environments
  • Spoken and written fluency in English

Desired Qualifications:

  • Knowledge of human genetics, GWAS and basic bioinformatics resources (Ensembl, the GWAS Catalog, genome annotation and expression databases…)
  • Practical experience with omics data analysis
  • Familiarity with common statistical genetics tools (e.g. plink, bcftools) 

What we offer you

scientific training at HELENA Graduate School
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work-life balance
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flexible working hours & time off in lieu
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30 days annual leave
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on-site health management service
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Munich, with its numerous lakes and its vicinity to the Alps, is considered to be one of the cities with the best quality of life worldwide. With its first-class universities and world-leading research institutions it offers an intellectually stimulating environment.

This position also provides the opportunity for you to build specialist knowledge and collect significant professional experience, both of which will help advance your scientific career.

Remuneration and social benefits are based on the collective wage agreement for public-sector employees at federal level (EG 13 65% TV EntgO Bund). The position is (initially) limited to three years, but under certain circumstances an extension can be arranged.

To promote diversity, we welcome applications from talented people regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical abilities, religion and age. Qualified applicants with physical disabilities will be given preference.
If you have obtained a university degree abroad, we require further documents from you regarding the recognition of the degree. Please request the recognition as early as possible.

Interested?
If you have further questions, simply contact Dr. Iris Fischer, 089 3187-3822, who will be happy to be of assistance.
Helmholtz Zentrum München
Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)
Institute of Translational Genomics
Ingolstädter Landstraße 1 

85764 Oberschleißheim

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