The Medical Bioinformatics group of the Medizinisches Proteom-Center (MPC) of the Ruhr-University Bochum is looking for a (Bio-)Informatician, Statistician or Data Analyst/Machine Learning Engineer (m/f/d) for the next possible date until 31.12.2022. Within the research project CovidDataNet.NRW the hired scientist will predict the course of the disease as well as the disease subtype of the patients by using machine learning methods. Through this work, you can contribute to individualized and improved treatment of Covid-19-patients.

Since 2019, the MPC is part of the research building “PRODI - molecular protein diagnostics” as the competence area “Medical Proteome Analysis”. The Medical Bioinformatics group conducts research in the field of bioinformatics for proteomics and statistically analyzes quantitative proteomics data using own tools and algorithms. The (RUB) is one of the leading research universities in Germany. As a reform-oriented campus university, it uniquely combines the entire range of the major scientific fields in one place. The dynamic interaction of disciplines and faculty cultures offers researchers and students alike excellent opportunities for interdisciplinary cooperation.


- Implementation and application of unsupervised machine learning methods (e.g. clustering) for identification of disease subtypes.
- Development and implementation of procedures for feature selection with methods from statistics and machine learning to identify information-carrying variables. The combination of those may serve as a prognostic or diagnostic biomarker for the course of disease.
- Development and implementation of a suitable modelling procedure and of a software module for predicting the disease subtype and the course of disease in the future field of individualized medicine.
- Bioinformatical and statistical analysis of clinical, proteomics and other omics data, which are generated during a clinical research project.

We offer:

- Employment at one of Germany's largest universities in the University Alliance Ruhr network
- Work in a highly relevant clinical research project
- Cooperation in an interdisciplinary and international team
- A meaningful employment which contributes to the improvement of Covid-19 treatment

Your profile:

You have a degree (diploma/master) in (bio-)informatics, statistics, data science or similar. Furthermore you have knowledge in and/or experience with the following areas of scientific data analysis in the life sciences:

- Statistics and machine learning (unsupervised and supervised)
- Algorithms and methods for feature selection, classification and clustering
- Analysis of biological high-throughput and omics data
- Programming in at least one of the languages R, Python or a similar scripting language

Ideally, you have knowledge and interest in:

- Methods for data cleaning and data preprocessing
- Methods for the analysis of black-box models (feature importance, explainable machine learning)
- Data and result visualization
- Induction into proteomics and mass spectrometry

You also bring the following skills:

- Independent acquisition and application of new methods/knowledge
- Independent and structured way of working with good time management skills
- Good interdisciplinary communication skills
- Good written and spoken English
- Please send your convincing application via e-mail (in a single pdf file) to Miss Karin Schork (This email address is being protected from spambots. You need JavaScript enabled to view it.) until 25.11.2021.

If funding at the time of hire is provided solely by external third-party funding sources, employees are not required to assume teaching responsibilities.

At the Ruhr-University Bochum, we want to particularly promote the careers of women in those areas in which they are underrepresented and are therefore very pleased to receive applications from female applicants. We also welcome applications from suitable severely disabled and equal opportunities applicants. Travel expenses for possible job interviews cannot be reimbursed. Application documents can only be returned if a self-addressed and stamped envelope is enclosed.

Information on the collection of personal data in application procedures:

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