Within the Discovery Sciences group, which provides advanced technology support to drug discovery projects in Janssen, the Computational Sciences department is responsible for comprehensive analyses of high-
dimensional datasets and MultiTask and DeepLearning machine learning applied to small molecule programs. The position will be opened on the Beerse campus, which is the flagship R&D center for small molecules within Janssen, investing over 1 billion euros each year in R&D.
Activities cover the integration of information from the broadest possible spectrum of datasources (chemical structure, microscopy images and omics, ) to comprehensively infer biological activities of millions of chemical compounds, and to apply these predictions to inform projects.
In addition, the team designs and supports the creation and application of deep learning and generative approaches for suggesting novel compounds to make, as well as the creation and application of deep learning methods for retrosynthesis and forward synthesis approaches.
Janssen is looking for a candidate who can interact with medicinal chemists to discuss novel compound suggestions to be synthesized, as well as improve the deep learning approaches to reflect a medicinal chemistry test and design cycle more smoothly.
Job description In the context of the latter task, the department is opening a position for a post-doctoral fellow data sciences and machine learning for molecule design and synthesis who will focus on the creation and application of deep learning approaches to inform medical chemistry projects.
The post-doctoral fellow will do world-class research in generative approaches for suggesting novel compounds to make, as well as the creation and application of deep learning methods for retrosynthesis and forward synthesis approaches.
This will also include the incorporation of high-dimensional datasets to identify interventional strategies for neurological, cardiovascular and metabolic, infectious, immunological or oncological diseases.
The candidate will be responsible for
doctoral experience in an academic or industrial setting)
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