PhD positions available in Big Data Analysis in Chemistry

This position is announced within the BIGCHEM project. Read about the carrier development perspectives.

Check eligibility rules as well as recruitment details and apply for this position before 20 March 2016.

Objectives: In the life-sciences, data is being generated and published at unprecedented rates. This wealth of data provides unique opportunities to get insights into the mechanisms of disease and to identify starting points for treatments. At the same time, the size, complexity and heterogeneity of available data sets pose substantial challenges for computational analysis and design.

Aim of this project is to address the challenges posed by large, heterogeneous, incomplete, and noisy datasets. Specifically, we aim to:

• apply machine learning technologies to derive predictive QSAR models from real-world life science data sets;
• analyze trade-offs between training data accuracy and quantity, in particular, in the context of high-throughput screening data;
• develop and apply methods to systematically account for noise and experimental errors in the search for active compounds.

Planned secondments: Three months stay in NextMove to work with data automatically extracted from patents using unique technology of company. Three months in HMGU to collect data from public databases such as ChEMBL, OCHEM, PubChem.

Employment: 36 months total, including Boehringer Ingelheim, Biberach, Germany (months 1-18) and the University of Bonn, Germany (months 19-36).

Enrollment in PhD program: The ESR will be supervised by Prof. J. Bajorath from the University of Bonn and by supervisors from Boehringer Ingelheim.

Salary details are described here.