Scientist (Drug Discovery), BII

Job Summary

Do you want to bridge the gap between methodological innovation and real impacts in drug discovery?

We are seeking a passionate Computational Scientist to join our interdisciplinary research team focused on the integration of Computation, AI/Machine Learning, and Drug Discovery. You are a PhD-level researcher with a strong track record in developing novel computational methodologies and motivated by the research that make transformative impacts on how medicines are discovered and developed.

RESPONSIBILITIES

• Design and deploy innovative computational approaches - integrating physics-informed, biology-informed, causal and uncertainty-aware machine learning - to accelerate and de-risk key stages of drug R&D, including target/biomarker identification, molecular optimization, translational predictive modeling.
• Develop and optimize computational frameworks that integrate diverse data types (chemical, biological, omics, clinical) into cohesive models.
• Collaborate with domain experts in computational biology, cheminformatics, pharmacology, and drug discovery to tailor computational models to real-world problems.
• Publish research findings in leading journals and conferences, and contribute to partnerships and strategic initiatives as opportunities arise.
• Mentor junior team members and contribute to a collaborative, cross-disciplinary research environment.
  
  

REQUIREMENT

• PhD in Artificial Intelligence/Computer Science, Bioinformatics, Computational Biology, Biomedical Engineering, Applied Mathematics, Pharmaceutical Sciences or a related field, with a focus on machine learning or computational modeling.
• Strong publication record or demonstrable contributions to open-source tools or reproducible research.
• Demonstrated expertise with AI/ML methodologies and implementations.
• Excellent problem-solving skills, with an ability to balance theoretical rigor with practical implementation.

PREFERRED QUALIFICATIONS

• Familiarity with challenges in drug discovery and development.
• Research interest in areas of AI/ML such as Multi-Agent Systems, Physics-Informed ML, Causal AI, Neuro-Symbolic AI, Uncertainty Quantification, Active Learning, Geometric Deep Learning.