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Advertising division: INM-9 - Computational Biomedicine
Reference number: 2019D-136


The Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) provides an interdisciplinary environment for educating the next generation of data scientists in close contact to domain-specific knowledge and research. All three domains – life & medical sciences, earth sciences and energy systems/materials – are characterized by the generation of huge heterogeneously structured data sets, which have to be evaluated in order to obtain a holistic understanding of very complex systems. Visit HDS-LEE at:

We are currently searching students interested in combining data-science approaches with physics-based techniques for the identification of novel candidate modulators of pain. Pathway-based drug discovery approaches and in silico pharmacology will be applied. The project is in collaboration with Grünenthal Pharma Industry.

We are offering a

PhD Position "Pathway-Based drug discovery of novel analgesics"

Your Job:

  • Use of NLP/text mining approaches to extract relevant interactions that can be assembled into more complex pathways building an adjacency matrix
  • Biological pathway analysis using Metacore starting from lists of genes
  • Extract pathway information in mathematical form, e.g. network node relations
  • Compare and analyze with graph theoretical models using available programs e.g. R-packages (iGraph)
  • Extend existing graph theoretical packages to merge and compare biological pathways
  • Export pathway information into a format suitable to work with tensorflow/keras
  • Prioritize and rank identified pathways by descriptors and probabilistic methods
  • Use unsupervised learning to generate abstract representations of pathways (e.g. autoencoder)
  • Build and optimize supervised classifiers
  • Molecular simulation on identified pathway entities, disease specific
  • Virtual screening and identification of potential therapeutic interventions
  • Validate results by pathway-based repurposing of drugs and in vitro/in vivo tests

Agenda / Work plan:
1st year: knowledge organization and representation
2nd year: machine learning of classifiers, regressors, and pathway abstractions
3rd year: simulation, screening

Your Profile:

  • University degree in either physics, chemistry, applied mathematics or computer science
  • Experience with UNIX-like operating systems
  • Mathematical and programming skills (R, Python, Keras,Tensorflow)
  • Ideal prior knowledge on pathway/Systems biology or MD simulations
  • Excellent knowledge of written and oral English: TOEFL or equivalent evidence of English-speaking skills
  • Interactive person with good communication skills
  • Used to work in international teams
  • A high level of scholarship as indicated, for example, by bachelor and master study transcripts and two reference letters

Our Offer:

  • Outstanding scientific and technical infrastructure – ideal conditions for successfully completing a doctoral degree
  • Unique HDS-LEE graduate school program
  • A highly motivated group as well as an international and interdisciplinary working environment at one of Europe’s largest research establishments
  • Chance of participating in (international) conferences
  • Continuous scientific mentoring by your scientific advisor
  • Further development of your personal strengths, e.g. via a comprehensive further training program
  • Pay in line with 100 % of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund)
  • A contract for the duration of 3 years

Forschungszentrum Jülich aims to employ more women in this area and therefore particularly welcomes applications from women.

We also welcome applications from disabled persons.

Additional Information

We look forward to receiving your application, preferably online via our online recruitment system on our career site until 30.09.2019, quoting the above-mentioned reference number.

Questions about the vacancy?
Contact us by mentioning the reference number 2019D-136:
Please note that for technical reasons we cannot accept applications via email.