2027 Future Talent Program – Translational Sciences and Outsourcing – Co-op
Core
Develop and benchmark AI-enabled and hybrid modeling approaches to predict toxicokinetics (TK) and exposure at untested dose levels in preclinical species, supporting drug metabolism and pharmacokinetics (DMPK) decision-making.
Role type
Co-op translational modeling scientist (AI/ML)
Builds
Reusable benchmark datasets and model-comparison reports for preclinical PK/TK prediction
Domain
Pharmaceutical R&D, Translational Sciences, Preclinical Toxicology
Deliverable
production ML models | dashboards & analysis
Required skills
Python (NumPy, pandas, scikit-learn), Deep Learning (PyTorch, JAX), Machine Learning, Statistical Modeling, Data Curation, Scientific Computing
Preferred skills
Graph Neural Networks, Molecular Representation Learning, Cheminformatics (QSAR, ChemProp), Mechanistic Modeling (ODEs, PBPK), Agentic AI/LLM tooling
Technologies
PyTorch, JAX, scikit-learn, SciPy, KNIME, torchdiffeq
Responsibilities
Curate and quality-check preclinical cross-dose PK/TK datasets; Conduct TK analysis to identify trends and nonlinearities; Develop and benchmark AI-enabled and hybrid modeling approaches; Evaluate model performance and uncertainty estimation; Create visualizations and technical presentations for stakeholders; Share project outcomes via internal/external presentations and publications
Seniority
Student (Master's/Ph.D.), Research Co-op
