Assistant Professor for Computational Non-Target Screening of Contaminants of Emerging Concern Full-time NEW

Assistant Professorship In Machine Learning–Based Non-Target Screening Of Contaminants

The Analytical Chemistry Group invites applications for a 4-year assistant professorship in machine learning–based non-target screening of contaminants, CECs and other complex chemical mixtures.

The position is within analytical chemistry with a focus on machine learning–based non-target screening of contaminants of emerging concern (CECs) and related chemical fingerprints in complex environmental and biological matrices. The research combines high-resolution mass spectrometry, complementary chromatographic separations (LC, online-SPE-LC, SFC and GC×GC), and analytical data science to develop scalable workflows for targeted, suspect and non-target screening. The aim is to transform large HRMS datasets from drinking water, wastewater, sludge, advanced treatment systems and human urine into chemical annotations, exposure signatures and understanding of contaminant fate, removal and human exposure.

The position will contribute to two closely connected research directions: identification of micropollutants, including highly polar, persistent, mobile and fluorinated compounds, in wastewater, sludge and advanced treatment systems to support machine-learning models for contaminant fate and removal; and large-scale profiling of the human urinary exposome to identify chemical exposure signatures associated with disease.

The research is based on complementary chromatography-HRMS workflows, including LC, online-SPE-LC, SFC and GC×GC coupled to Orbitrap, QqTOF and TOF platforms. A central task is to develop reproducible and scalable data workflows that integrate automatic preprocessing, cheminformatics, chemical databases and machine learning for compound identification, annotation, prioritization, confidence annotation, and quantitative or semi-quantitative interpretation.

The position has a strong analytical data science profile. We are particularly interested in candidates who can develop and critically validate open, transparent and reusable workflows for large HRMS datasets, including programming-based data processing, AI-assisted code development, workflow benchmarking, molecular pattern recognition, and integration of experimental analytical chemistry with predictive modelling.

We seek ambitious candidates with a strong profile in analytical chemistry, separation science, HRMS and/or analytical data science, and with motivation to work in close collaboration with academic, clinical, regulatory and industrial partners.

Who Are We Looking For?

Ideal applicants should have:

• A PhD in analytical chemistry, environmental chemistry, bioanalytical chemistry, metabolomics, exposomics, chemometrics, computational chemistry, analytical data science or a closely related discipline
• Documented experience with HRMS-based targeted, suspect and/or non-target screening workflows for complex environmental or biological samples
• Experience with large-scale analytical datasets, including feature detection, alignment, blank filtering, annotation, prioritization, benchmarking and quality control
• Experience with cheminformatics, including the use of chemical databases, spectral libraries, molecular fingerprints, in-silico prediction tools and computational approaches for compound annotation.
• Programming skills, for example in Python, R or MATLAB, for processing and interpretation of chromatography and mass spectral data. Experience with reproducible code, AI-assisted coding, prompt engineering, code validation and debugging is an advantage
• Experience with complementary chromatographic approaches such as LC, online-SPE-LC, SFC, GC or multidimensional chromatography is an advantage
• Experience with CEC analysis, highly polar or mobile contaminants, PFAS or organofluorine screening, wastewater, sludge, urine, exposomics and/or machine learning-based modelling is highly advantageous
• Documented experience with scientific writing, including peer-reviewed publications and grant or fellowship applications
• Proven ability to lead research activities independently, contribute strategically to interdisciplinary projects and collaborate constructively within a research group

The assistant professor's duties are research and teaching, including obligations with regard to publication/scientific communication, within Computational Non-Target Screening of Contaminants of Emerging Concern. To a limited extent this may also include performance of other duties.

Assessment of applicants will primarily consider their level of documented, internationally competitive research. The ability to attract external funding will be considered together with outreach qualifications. Teaching qualifications are not mandatory, but an interest in teaching is essential and documented teaching qualifications and teaching experience will be considered an advantage.

Further information on the Department can be found on the Department of Plant and Environmental Sciences – Institut for Plante- og Miljøvidenskab – Københavns Universitet . Inquiries about the position can be made to Jan H. Christensen, jch@plen.ku.dk.

The position is open from 1 November 2026 or as soon as possible thereafter.

The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.

Terms of employment The position is covered by the Memorandum on Job Structure for Academic Staff.

Terms of appointment and payment accord to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State.

Negotiation for salary supplement is possible.

The application, in English, must be submitted electronically by clicking APPLY NOW below.

Please include:

• Curriculum vitae
• Diplomas (Master and PhD degree or equivalent)
• Research plan – description of current and future research plans
• If available, description and documentation of teaching and supervision experience and qualifications – please describe and document:
  • Experience with supervision of BSc and MSc students
  • Teaching experience
  • Formal pedagogical training
• Complete publication list
• Separate reprints of 5 particularly relevant papers

The deadline for applications is 4 October 2026, 23:59 GMT +2.

After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee.

You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/