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We’re looking for a Computational Toxicologist to help us develop innovative, AI-powered solutions that predict toxicological risks and translate data into actionable insights.
The ideal candidate is a proactive and creative scientist that possess a solid foundation in cheminformatics and toxicology, proficient in programming languages like Python or R, and excel at collaborating and communicating complex scientific insights.
General Information
Planned duration: 1 year
Workplace: Basel
Tasks & Responsibilities
* Design, develop, and apply machine learning models to predict safety-relevant endpoints (e.g., liver or kidney toxicity) using chemical structure and biological data
* Integrate chemo-informatics and in vitro safety data, with the potential to expand toward transcriptomics or other omics technologies
* Provide in silico support for discovery and early development programs, offering scientific insights into potential safety risks
* Leverage internal data and external knowledge bases to enhance model performance and interpretability
* Collaborate closely with toxicologists, pharmacologists, data scientists, and chemists to co-create solutions and ensure models are meaningful and relevant
* Contribute to broader efforts such as biological read-across, reverse translation of historical data, and refinement of digital workflows for safety decision-making
Qualification and experience
* PhD or MSc (with relevant experience) in Computational Toxicology, Cheminformatics, Bioinformatics, Data Science, Pharmacology, or a related field
* Solid experience in developing machine learning models, ideally applied to chemical and biological data
* Strong foundation in cheminformatics/chemistry, including working with molecular descriptors, chemical similarity, and structure-based analyses
* Experience with toxicological datasets and safety endpoints such as DILI or nephrotoxicity
* Familiarity with in vitro safety data and an interest in integrating complex biological datasets
* Proficient in programming (e.g., Python, R) and using scientific computing libraries (e.g., RDKit, scikit-learn, Pandas, TensorFlow, or similar)
* Excellent communication and collaboration skills; able to translate technical insights for interdisciplinary teams
* Experience with toxicological datasets and safety endpoints such as DILI or nephrotoxicity
* Understanding of omics data integration or biological pathways related to toxicology
* Familiarity with pharmaceutical R&D or prior experience in industry (a plus, but not essential)
Deadline: 13.06.2025
Seniority level
* Seniority level
Mid-Senior level
Employment type
* Employment type
Contract
Job function
* Job function
Information Technology
* Industries
Pharmaceutical Manufacturing and Biotechnology Research
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