PhD Fellowship: Computational Pharmacology & Data Science

DTU — Technical University of Denmark; København; On-site; DKK 233,000 - 285,000; Full time

Job summary

The Department of Drug Design and Pharmacology at the University of Copenhagen invites applications for a three-year PhD fellowship in computational pharmacology and pharmaceutical data science, commencing 15 October 2026.

The project combines MD simulations, GPCR analysis, and machine learning to predict ligand- and variant-specific pharmacological outcomes, using the dopamine D2 receptor as the primary model.

Qualifications

• Relevant experience with molecular modelling, molecular dynamics simulations and/or analysis of molecular simulation data.
• Excellent programming skills, preferably in Python including code versioning in GitHub.
• Experience with statistical analysis and/or machine-learning methods.
• Knowledge of protein structure, structural bioinformatics, or molecular interactions; experience with GPCRs or membrane proteins is an advantage.
• Strong written and spoken English and good communication and collaboration skills.

Responsibilities

• Relocate to Copenhagen and lead the research project on a day-to-day basis.
• Present research in internal meetings and international conferences.
• Take an active part in manuscript writing and dissemination activities.
• Translate new knowledge in the subject area into research activity.
• There will be the opportunity to supervise more junior group members and contribute to teaching.

Skills

Molecular modelling

Molecular dynamics simulations

Data analysis

Python programming

Statistical analysis

Machine learning

English communication

Education

Master’s degree (180 ECTS BSc + 120 ECTS MSc) or equivalent

Tools

GitHub

Job description

The Department of Drug Design and Pharmacology at the University of Copenhagen invites applications for a three-year PhD fellowship in computational pharmacology and pharmaceutical data science, commencing 15 October 2026.

The project combines MD simulations, GPCR analysis, and machine learning to predict ligand- and variant-specific pharmacological outcomes, using the dopamine D2 receptor as the primary model.