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The PositionBy pioneering data-driven technology and insights, and connecting early diagnosis to targeted treatments, we’re advancing science to ensure everyone has access to the healthcare they need. Within Roche Pharma Research & Early Development (pRED), the ADME chapter is responsible for the prediction of the effect of newly developed medicines on the human body. This role offers a unique opportunity to collaborate with a diverse group of industry and academic scientists in a cross-functional project at the interface between pharmacokinetics and machine learning. This postdoctoral research position is a highly collaborative project focusing on building bridges between experimentalists and data scientists within pRED and also with our external collaborators.
The Opportunity
Develop, validate, and operationalize machine learning models to accurately predict drug-drug interactions (DDI).
Build a flexible, reusable Python framework that can be adapted for various datasets beyond DDI prediction.
Explore and implement innovative methods for handling data imbalance and low-data regimes.
Collaborate with scientists across interdisciplinary fields, including ADME and small molecule data analytics.
Engage with external stakeholders to perform method validation.
Publish your findings in leading scientific journals to contribute to the advancement of machine learning in drug discovery.
Participate in a supportive and dynamic team environment that fosters innovation and values diverse perspectives to drive impactful research.
Who you are
Essential
PhD degree in machine learning, cheminformatics or a related field
To be eligible for a Roche Postdoctoral Fellowship, you must be within the first four years of completing your PhD
Fluency in Python and proficiency with cheminformatics tools such as RDKit and OpenBabel
Deep experience in machine learning and cheminformatics, with a proven track record (e.g. relevant publications), ideally demonstrated through at least one public GitHub repository and related first author publication
A collaborative mindset
The ability to work independently and effectively in an international team at the intersection of academia and industry.
Excellent communication skills in English (both written and spoken).
Desirable
An understanding of DDI and drug metabolism
Ideally experience in a specialised area of machine learning (e.g. deep learning, multi-task learning, conformal prediction) applied to small molecule challenges
This position offers a unique opportunity to work at the intersection of machine learning and drug discovery, contributing to cutting-edge research in a collaborative and interdisciplinary environment. The RPF project is initially set for a duration of two years, with the possibility of a third-year extension. The location is Basel.
Join Us
Embark on a transformative career path where your contributions will help shape the future of healthcare. Click on the \"Apply online\" button below to apply. All applications need to include a CV, motivation letter, a publication list and, if available, your PhD certificate.
More information about the RPF Program can be found here.
Who we areAt Roche, more than 100,000 people across 100 countries are pushing back the frontiers of healthcare. Working together, we’ve become one of the world’s leading research-focused healthcare groups. Our success is built on innovation, curiosity and diversity.
Basel is the headquarters of the Roche Group and one of its most important centres of pharmaceutical research. Over 10,700 employees from over 100 countries come together at our Basel/Kaiseraugst site, which is one of Roche`s largest sites. Read more.
Besides extensive development and training opportunities, we offer flexible working options, 18 weeks of maternity leave and 10 weeks of gender independent partnership leave. Our employees also benefit from multiple services on site such as child-care facilities, medical services, restaurants and cafeterias, as well as various employee events.
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