Johnson & Johnson Careers
Post-doc ML/AI for small molecule invention
Requisition ID: 1805706089W
Post-doctoral position in machine learning and AI for small molecule invention
Janssen Research & Development seeks to drive innovation and deliver transformational medicines for the treatment of diseases in six therapeutic areas: neuroscience, cardiovascular diseases and metabolism, infectious diseases, immunology, oncology and pulmonary hypertension. In these areas, Janssen aims to address and solve unmet medical needs through the development of small and large molecules, as well as vaccines. The Janssen campus in Beerse (Belgium) has a unique ecosystem covering the complete drug development life cycle, with all capabilities from basic science to market access on one campus. The integrated environment of our campus gives our people the chance to experience many different aspects of drug development throughout their career. It has a successful track record of over sixty years of drug discovery and development and is one of the most important innovation engines of the Janssen group worldwide.
Developing innovative therapeutics to treat diseases like Alzheimer’s disease, various types of cancers and infectious diseases like Hepatitis B, influenza is our passion. In this endeavor, we are seeking to recruit new talent for the comprehensive analyses of high-dimensional datasets using state-of-the-art data science methods applied to drug discovery programs. Two full-time two-year positions 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.
These two positions frame in the ongoing virtualization of parts of the research process. The successful candidates will join a team of over a dozen data scientists who introduce large scale machine learning and artificial intelligence to leverage the extensive datasets accessible to the company. Accessible datasets include chemical descriptions and annotations of desired and undesired biological activities for millions of small molecules across thousands of assays, and information-rich but hard-to-interpret documentation of small molecules like microscopy images or transcriptomics profiles acquired at high throughput (hundreds of thousands of profiles in several assays). In addition, dozens of millions of chemical reactions can be mined. The goal is the selection and design of small molecules to make and test en route to better and safer drugs for patients with unmet medical needs.
The two post-docs will contribute to improve Janssen’s ability to predict the biological activity of small molecules, and their reactivity in chemical reactions to form new compounds.
One of the two post-docs will focus on research and application of advanced secondary modeling approaches that complement primary predictive models with confidence and information-gain estimates. The developed framework will help to dynamically adjust the data source combination and predictive modalities to deploy for the predictive tasks at hand and to inform and design new data generation efforts. The second post-doc will focus on the introduction of machine learning-accelerated simulation technologies in the predictive frameworks.
The successful candidates will be responsible for
- design, development, internalization and combination of state-of-the-art machine learning methods to select and design small molecules to make and or test
- finding and evaluating new and creative ways of unlocking information from accessible data sources and theoretical concepts
- translation of questions of biologists and chemists to a quantitative analysis formulation
- interaction with R&D informatics to make their proof-of-concept solutions robustly accessible to users, with visualization solutions
- interaction with colleagues from computational chemistry, drug metabolism and pharmacodynamics and toxicology to account for their insights and needs in methodological design
- contributing to the scientific eminence of the department by authoring peer-reviewed papers and presenting at relevant conferences
- PhD in machine learning with Master level training in organic chemistry, biochemistry, cell biology or pharmacology, or PhD in organic chemistry, biochemistry, cell biology or pharmacology with Master level training in machine learning or equivalent interdisciplinary training in related quantitative fields
- advanced programming and scripting skills that enable the development of functional prototypes
- experience with advanced machine learning frameworks, like PyTorch, Keras, Tensorflow
- excellent communication, reporting and team interaction skills, self-motivation, proactivity and the ability to work independently
Janssen Pharmaceutica N.V. (7555)