Postdoc i vibrational spektroskopi og maskinlæring for farmaceutiske materialer

We are looking for a highly motivated and dynamic postdoc for a 2-year position, who will develop and apply advanced data analysis methodologies for low-frequency Raman and Brillouin spectroscopy of pharmaceutical materials, to commence 1st of May 2026 or as soon as possible thereafter.

The position focuses on the methodological development of chemometric and machine learning frameworks tailored to low-frequency vibrational and mechanical spectroscopy, and their application to complex, multicomponent pharmaceutical systems.

Information on the department can be found at: https://pharmacy.ku.dk

Our research

Research in the Pharmaceuticals, Processes and Products (3P) group focuses on structure–property–performance relationships in pharmaceutical materials, with particular emphasis on solid-state and mechanical properties relevant for stability, processing, and drug-release behavior. Using a supportive and interdisciplinary team-based approach, and embedded in an international network of collaborators, we combine advanced experimental characterization and computational modelling to study systems ranging from small molecules to polymeric, lipid-based, and protein-containing formulations.

Within this framework, the group is expanding activities in advanced data analysis for low-frequency Raman (LFR) and Brillouin spectroscopy using chemometrics and machine learning. The successful applicant will join a large and active 3P research environment and will be fully embedded in the Center for Pharmaceutical Data Science Education (CPDSE; https://www.cpdse.dk), a cross-university KU–SDU network with more than 60 active members across seven departments.

Your job

We strive to further advance quantitative and physically grounded analysis strategies for LFR and Brillouin spectroscopy. Your tasks would be to consolidate and extend existing data analysis approaches into a coherent and robust framework, combining experimental design with theoretical and data-driven modelling.

In particular, you will:

• Refine and standardize preprocessing and normalization workflows for LFR and Brillouin data.
• Develop and validate feature-extraction strategies that translate low-frequency vibrational and mechanical signatures into physically interpretable descriptors of molecular mobility, phase heterogeneity, and mesoscale organization.
• Implement and benchmark chemometric and machine learning models (including one-dimensional convolutional neural networks) optimized for low-frequency spectroscopic inputs.
• Integrate spectroscopic data with complementary characterization techniques, including scattering methods, using multivariate and data-fusion approaches.
• Apply and further validate the framework in pharmaceutical systems beyond small molecules, with emphasis on polymeric, lipid, and protein-based formulations, supporting and extending pioneering proof-of-concept studies within the group.
• Contribute to experimental Brillouin and LFR measurements, as well as in situ or operando characterization strategies, to ensure data quality and relevance for modelling in these emerging application scenarios.
• Contribute to the supervision and scientific mentoring of PhD and Master’s students working in related areas, and to the development of shared analysis standards within the group.

Profile

We are looking for a highly motivated and enthusiastic scientist with the following competencies and experience:

Essential experience and skills:

• You have a PhD in Pharmaceutical Sciences, Chemistry, Physics, Materials Science, or a closely related experimental discipline.
• You are highly experienced in the analysis of spectroscopic and/or physicochemical data using chemometric and/or machine learning approaches, as evidenced by publications.
• You have an active interest in developing quantitative data analysis and modelling tools for experimental spectroscopy.
• Proficient communication skills and ability to work in interdisciplinary teams.
• Excellent English skills, written and spoken.

Desirable experience and skills:

• Experience with LFR, Brillouin, or related vibrational and mechanical spectroscopy techniques.
• Experience with pharmaceutical solid-state materials and/or multicomponent formulations.
• Experience in developing reusable and well-documented data analysis workflows or software tools.
• Experience with multivariate data analysis, data fusion, or deep learning methods applied to experimental data.
• Experience with complementary scattering techniques (e.g. X-ray diffraction, SAXS/WAXS), particularly for in situ or operando studies.
• Experience evidenced by publications in relevant areas.

Place of employment

The place of employment is at the Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen. We offer creative and stimulating working conditions in a dynamic and international research environment.

Our research facilities include modern laboratories and access to state-of-the-art infrastructure, including CPHARMA (https://pharmacy.ku.dk/research/cpharma/) and an extensive international partner network (https://www.pssrc.org/). In addition, the successful applicant will be fully embedded in CPDSE (https://cpdse.ku.dk/), supporting interdisciplinary collaboration in pharmaceutical data science.

Terms of employment

The average weekly working hours are 37 hours per week.

The position is a fixed-term position limited to a period of 2 years. The starting date is 1st of May 2026 or as soon as possible thereafter.

Salary, pension and other conditions of employment are set in accordance with the Agreement between the Ministry of Taxation and AC (Danish Confederation of Professional Associations) or other relevant organisation. Currently, the monthly salary starts at 35,000 DKK/approx. 4,700 EUR (October 2021 level). Depending on qualifications, a supplement may be negotiated. The employer will pay an additional 17.1 % to your pension fund.

Foreign and Danish applicants may be eligible for tax reductions, if they hold a PhD degree and have not lived in Denmark the last 10 years.

The position is covered by the Job Structure for Academic Staff at Universities 2020.

Questions

For further information please contact Assistant Prof. Kārlis Bērziņš; [beverlydaviskarlis.berzinsjohnsonbrenda@sund.kunelson-petersmcconnell.dk]; www.sund.ku.dk

Foreign applicants may find this link useful: www.ism.ku.dk (International Staff Mobility).

Application procedure

Your online application must be submitted in English by clicking ‘Apply now’ below. Furthermore, your application must include the following documents/attachments – all in PDF format:

1. Motivated letter of application (max. one page).
2. CV incl. education, work/research experience, language skills and other skills relevant for the position.
3. A certified/signed copy of a) PhD certificate and b) Master of Science certificate. If the PhD is not completed, a written statement from the supervisor will do.
4. List of publications.

Deadline for applications: 28/02/2026, 23.59pm CET

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.

The further process

After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the hiring committee. All applicants are then immediately notified whether their application has been passed for assessment by an unbiased assessor. Once the assessment work has been completed each applicant has the opportunity to comment on the part of the assessment that relates to the applicant him/herself.

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

The applicant will be assessed according to the Ministerial Order no. 242 of 13 March 2012 on the Appointment of Academic Staff at Universities.

Interviews are expected to be held in week 12 (March 2026).

The University of Copenhagen wish to reflect the diversity of society and encourage all qualified candidates to apply regardless of personal background.