Assistant Professor, Computational Non-Target Screening

Department of Pharmacy, University of Copenhagen; Pludual; Sur place; EUR 94 000 - 120 000; Plein temps

Résumé du poste

The University of Copenhagen invites applications for a four-year assistant professorship in machine learning–based non-target screening of contaminants of emerging concern (CECs) and related chemical fingerprints. The role combines analytical chemistry, data science, and scalable workflows across HRMS, chromatography, and cheminformatics.

Duties include research and teaching, with a focus on publication, funding, and collaboration with academic, clinical, regulatory and industrial partners.

Qualifications

• PhD in analytical chemistry or related field with focus on MS-based workflows.
• Experience with HRMS-targeted, suspect and non-target screening in complex samples.
• Proven ability to work with large analytical datasets and QC workflows.

Responsabilités

• Lead development of ML-based non-target screening workflows for CECs.
• Publish findings and present at conferences, and contribute to grant applications.
• Collaborate with partners across academia and industry on data science projects.

Connaissances

HRMS data workflows

Machine learning

Programming (Python, R, MATLAB)

Data science

Cheminformatics

Formation

PhD in analytical chemistry

Experience with environmental/biological matrices

Teaching experience

Outils

Orbitrap

Q-TOF

TOF platforms

Python

R

MATLAB

Description du poste

The University of Copenhagen invites applications for a four-year assistant professorship in machine learning–based non-target screening of contaminants of emerging concern (CECs) and related chemical fingerprints. The role combines analytical chemistry, data science, and scalable workflows across HRMS, chromatography, and cheminformatics.

Duties include research and teaching, with a focus on publication, funding, and collaboration with academic, clinical, regulatory and industrial partners.