Our team is looking for a postdoctoral fellow with a strong background in computational biology and machine learning. The candidate shall implement spatial transcriptomics and machine learning into toxicology workflows. Apply here.
In this position, the postdoc will process spatial transcriptomics (ST) datasets from mouse and human tissues with defined morphological structures and high relevance for toxicology readouts. The candidate will develop integrative analysis approaches of morphological patterns, cell identity and gene expression using deep learning methods. The output will deliver precise organ structure annotations and further provide cell type composition, spatial cell clustering, pathway activities and cell communication readouts. The postdoc will further help generate a ST database that provides an invaluable reference to assess compound-associated changes and to evaluate their translational relevance for patients.
This position is sponsored by the Roche Postdoctoral Fellowship Programme. The position is funded for two years, with the possibility of a third-year extension. The candidate works in a cross-functional spatial transcriptomics team consisting of members of the Predictive Modeling and Data Analytics (PMDA) and Pathology chapters, part of Roche Pharma Research and Early Development (pRED) Pharmaceutical Sciences department. The University of Heidelberg, Germany and the Swiss Institute of Bioinformatics, Lausanne, Switzerland are Roche’s academic partners for this Postdoctoral Fellowship.
In the position, you will
- Support the analysis of ST data in toxicology related projects.
- Integrate morphological features and gene expression profiles at the tissue level through machine-learning models to precisely annotate organ structures.
- Help our team to develop a database and store the results.
- Publish the main results of your work in peer reviewed journals.
Requirements for the position:
- PhD (obtained within the last 4 years) in computational biology, bioinformatics, statistics, cheminformatics or related fields with experience in an academic or industry setting in the area of machine learning and/or omics data analysis.
- Proficiency in working with bioinformatics and statistical tools and methods as well as a strong background in machine learning and statistics.
- Proficient in Python, R or an equivalent programming language.
- Experience with digital pathology is beneficial but not required.
- Very good interpersonal and communication skills in English.
- You have been actively engaged in scientific research continuously since your PhD and ready to start an RPF postdoctoral activity no later than 4 years after the PhD.
The job starts in October 2022, or upon availability. A CV, a motivation letter, a publication list, and references are required in your application. If you are interested, apply for the position here.