PhD: Cheminformatics & ML for Sustainable Chemistry

Syddansk Universitet; Odense; On-site; DKK 379,000 - 454,000; Full time

Job summary

University of Southern Denmark invites applications for a PhD position in Cheminformatics and Machine Learning within the SDU Life Cycle Engineering department. The project contributes to PFAROS, focusing on sustainable healthcare and safer chemical innovations through data-driven approaches and cross-disciplinary collaboration.

The successful candidate will work on collecting data, building predictive models, and integrating experimental results with computational workflows, under the PFAROS

Qualifications

• Master’s degree in chemistry, environmental science, chemical engineering, computer science, data science, or related field.
• Strong interest in machine learning, cheminformatics, data science, and chemical risk assessment.
• Experience with programming in Python and scientific computing.
• Familiarity with machine learning methods, data management, and statistical analysis.
• Experience with chemical databases, molecular descriptors, QSAR models, or cheminformatics tools is an advantage.
• Excellent written and oral communication skills in English.

Responsibilities

• Collect, curate, and validate data for chemicals in commerce.
• Evaluate applicability domains and coverage of existing ML models for chemical properties.
• Develop, train, validate, and benchmark new models for chemical property prediction.
• Contribute to integration of experimental data, computational methods, and ML workflows.
• Collaborate with international project partners and contribute to publications and proposals.

Skills

Python

Machine learning

Data science

English comms

Education

MSc degree

Tools

Cheminformatics tools

Job description

University of Southern Denmark invites applications for a PhD position in Cheminformatics and Machine Learning within the SDU Life Cycle Engineering department. The project contributes to PFAROS, focusing on sustainable healthcare and safer chemical innovations through data-driven approaches and cross-disciplinary collaboration.

The successful candidate will work on collecting data, building predictive models, and integrating experimental results with computational workflows, under the PFAROS