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At Syngenta Crop Protection, we are pioneering solutions that safeguard global food security while supporting sustainable agriculture. As a world market leader headquartered in Switzerland, we empower farmers with innovative crop protection technologies. Join our mission to revolutionize plant protection from seed to harvest by advancing predictive safety and strengthening early research decision-making through data-driven and mechanistic approaches.

Tasks

  • Partner with toxicologists, biologists, chemists, bioinformaticians, and data scientists to define priority questions for predictive safety.
  • Lead the development and evaluation of advanced AI/ML methodologies and predictive modelling approaches to address strategic safety questions.
  • Apply machine learning, artificial intelligence, statistical modelling, and knowledge-driven approaches to extract meaningful biological signals from large, complex, and heterogeneous datasets.
  • Translate modelling outputs into actionable scientific insight that supports project and portfolio decisions.
  • Evaluate emerging AI, modelling, and computational-biology methods, identifying applications that offer practical scientific value.
  • Communicate complex computational findings clearly and credibly to multidisciplinary audiences.

Requirements

  • PhD (or equivalent experience) in Computational Biology, Bioinformatics, Systems Biology, Computational Toxicology, Data Science, Computer Science, Biomedical Engineering, or a related discipline.
  • Demonstrated experience integrating diverse biological data types and extracting meaningful biological insight from complex, high-dimensional datasets.
  • Strong programming skills in Python and experience with modern data science and machine learning frameworks.
  • Proven experience in developing, validating and applying AI/ML, statistical, or computational biology approaches to address biological questions, while building reproducible computational workflows, analytical pipelines, and scientific software tools.
  • Self-starter with enthusiasm, flexibility and desire to learn and apply new skills.
  • Experience in computational toxicology, predictive safety, agrochemical research, pharmaceutical discovery, environmental science, or another applied research setting is desirable.
  • Familiarity with adverse outcome pathways, mode-of-action frameworks, systems toxicology, network biology, or causal inference is desirable.
  • Experience with chemical informatics, molecular descriptors, structural alerts, read-across, QSAR, or exposure- and hazard-modelling approaches is desirable.
  • Background in applying AI to biological or toxicological data, including multimodal learning, graph-based methods, foundation models, or representation learning, with experience in using phenotypic imaging, cell painting, transcriptomic, metabolomic, or other molecular data as supporting evidence for model development or mechanistic interpretation is desirable.

Benefits

  • A position contributing to valuable and impactful work in a stimulating and international environment.
  • A superb working environment with an open culture and diverse workforce where new ideas are always welcome.
  • The opportunity to work with and learn from highly qualified and experienced employees and gain scientific and technical excellence.
  • Learning culture (Together we Grow) and a wide range of training options.
  • Competitive pension fund plan, flexible working, attractive bonus system, onsite doctor, fitness room, canteen, and other benefits such as family friendly initiatives, child and family allowance.
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Über uns
Syngenta Group ist ein international tätiges Agrartechnologieunternehmen mit Hauptsitz in Basel und entwickelt sowie vertreibt Lösungen in den Bereichen Pflanzenschutz, Saatgut und landwirtschaftliche Technologien. Das Unternehmen unterstützt Landwirte mit innovativen Produkten und Dienstleistungen, um Produktivität und Nachhaltigkeit in der Landwirtschaft zu verbessern. Syngenta Group ist weltweit tätig und gehört zu den führenden Unternehmen im Agrarsektor.
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