MSD-posted 2 months ago
$39,600 - $105,500/Yr
Intern
5,001-10,000 employees

The Future Talent Program features Cooperative (Co-op) education that lasts up to 6 months and will include one or more projects. These opportunities in our Research and Development Division can provide you with great development and a chance to see if we are the right company for your long-term goals. The Computational Toxicology group within Nonclinical Drug Safety group at our Company combines toxicology expertise, data science, and AI/ML, including emerging Large Language Model (LLM) and generative AI applications, to enhance the prediction and mechanistic understanding of drug safety risks. Our vision is to accelerate safety assessments, support early drug discovery teams, and reduce reliance on in vivo testing through advanced computational approaches. We are seeking a highly motivated Co-op student to join the Computational Toxicology team at our Company Research Laboratories. This position offers an exciting opportunity to apply machine learning (ML), natural language processing (NLP), and LLM-based reasoning to real-world pharmaceutical safety data. You will contribute to the development of AI pipelines that mine and integrate multimodal toxicology datasets, spanning genomics, metabolomics, chemistry, in vitro/in vivo data, and biomedical literature, to generate mechanistic hypotheses and predictive models of toxicity.

  • Develop and evaluate predictive models to assess compound safety risks and identify toxicity mechanisms using multimodal pharmaceutical data.
  • Design and implement NLP/LLM pipelines (prompt engineering, fine-tuning, retrieval-augmented generation, etc.) to extract insights from unstructured reports and biomedical literature.
  • Build and deploy interactive dashboards (e.g., Streamlit, Dash) to visualize and communicate findings to toxicologists, chemists, and project teams.
  • Collaborate with multidisciplinary scientists across toxicology, chemistry, and data science to translate computational outputs into actionable safety insights.
  • Currently pursuing a Ph.D. in Computational Science, Data Science, Bioinformatics, Computational Biology/Chemistry, Computational Linguistics, or a related field.
  • Experienced with NLP and LLM frameworks (e.g., Hugging Face, spaCy, NLTK) and techniques such as prompt design, fine-tuning, and retrieval-augmented generation (RAG).
  • Proficient in Python, with strong skills in statistics, data wrangling (pandas/SQL), and model development (scikit-learn, PyTorch, TensorFlow).
  • Familiar with Git/GitHub workflows.
  • Passionate about applying AI to improve human safety, reduce animal testing, and transform decision-making in drug discovery.
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