Post-Doctoral Research Position in Digital & Computational Toxicology

Develop and implement FAIR-compliant data management strategies, standards and data management plans. Build, curate and maintain toxicological datasets and digital knowledge resources from public databases, scientific literature, omics datasets and regulatory resources. Develop and apply computational methods for analysing, integrating and visualising complex toxicological datasets. Evaluate, implement and further develop AI- and machine learning-based approaches for data curation, analysis and knowledge extraction. Develop and integrate Adverse Outcome Pathways (AOPs) and other mechanistic knowledge frameworks to support biology-informed risk assessment. Contribute to national and international collaborative research projects and grant proposals. Publish scientific results in leading peer-reviewed journals and present findings at international conferences. Support training activities, knowledge transfer and collaboration within the academic, regulatory and industrial landscape.

The Swiss Centre for Applied Human Toxicology (SCAHT), associated to the University of Basel, is seeking a highly motivated Postdoctoral Researcher in Digital & Computational Toxicology to strengthen SCAHT's activities in digital transformation of human-relevant and regulatory toxicology to next-generation risk assessment. We are looking for a scientist with strong background in toxicology and expertise in computational toxicology, data science, artificial intelligence and data governance. Ideally, the candidate has experience in applied science or a keen interest in applying their experience to applied research goals and questions. The successful candidate will contribute to the development of innovative digital infrastructures, computational workflows and AI-enabled tools that support the national and international toxicology community.

• PhD in Toxicology, Computational Toxicology, Data Science, Bioinformatics, Computational Biology or a related discipline.

• Demonstrated expertise in toxicology, or a strong interest in applying computational methods to toxicological research.

• Experience in computational data analysis using Python and/or R, including the handling, curation and integration of large biological or toxicological datasets.

• Experience in handling, curating and integrating large biological or toxicological datasets.

• Knowledge of database management, FAIR data principles and data governance.

• Excellent analytical and problem-solving skills.

• Excellent written and spoken English with strong communication skills.

• Experience with machine learning, artificial intelligence and/or natural language processing.

• Experience with reproducible computational research using tools such as Git, Docker and workflow management systems.

• Familiarity with public toxicological databases (e.g. AOP-Wiki, ToxCast, PubChem or ChEMBL).

• A track record of scientific publications.

• Knowledge of German and/or French.