Postdoc Position in Computer science/Computational biology/Bioinformatics

We are seeking a highly motivated and experienced full-time Postdoc to join the research group of Jessica Hjaltelin for a 2-year position (with the possibility of extension) from October 1st, 2025, or soon thereafter. The successful candidate will work on a project leveraging multimodal health registry data and artificial intelligence (AI) to enable early prediction of ovarian cancer onset.

Our research

The Hjaltelin Group develops machine learning and AI models for 1) early detection of complex diseases, 2) prognostic prediction of patient outcomes, and 3) explaining longitudinal symptoms and risk factors, with a focus on translating these insights into clinical practise. The group utilizes Danish longitudinal health registries and electronic patient records, comprising hospital diagnoses, primary care events, laboratory measurements, medications, and clinical images. Additionally, we integrate these multimodal data to molecular systems-level data such as protein levels and genomics. The group is highly collaborative within-department, with international research teams, and clinical experts to help translate results into clinical practice.

Place of employment

The Hjaltelin group is part of the Section for Health Data Science & AI (HDSAI) at the Department of Public Health, located at the Panum Institute. Our research facilities include modern offices and workspaces, and we offer a dynamic and creative research environment with many opportunities. The group conducts cutting-edge research using large multimodal health data, deep learning, and translational science. The section has a collaborative spirit and is interdisciplinary, covering bioinformatics, statistics, machine learning/AI, and computer science.

Project description

The postdoc will apply machine learning/deep learning approaches along with explainable AI methods for early prediction and understanding of ovarian cancer onset. The postdoc will investigate and stratify trajectories leading to ovarian cancer. The aim is to discover novel risk factors and early symptoms that can help identify high-risk patients early on to pioneer a new era for cancer prediction and screening. The postdoc will lead the project while receiving continuous support from the PI and fellow group/section members. Access to data sets and the GPU supercomputing environment is readily available for immediate use.

Essential experience and skills

• PhD in Computer Science, Computational Biology, Bioinformatics, or related field
• Hands-on experience and expert knowledge in deep learning algorithms (Transformers, RNNs, CNNs, etc.)
• Strong programming expertise in Python (PyTorch, TensorFlow, Explainable AI techniques, etc.) and R
• Solid experience with Linux/UNIX operating systems (command line scripting, highperformance computing clusters, and GPU-based training)
• Experience in integrating and analyzing large health data sets (EHRs, health registries, etc.)
• Handling of large data sets using databases such as PostgreSQL, DuckDB or similar.
• Formal training in mathematics and statistics (dimensionality reduction, hypothesis testing, clustering, survival analysis etc.)
• Experience with reproducible analyses and workflow documentation (Git, Bitbucket, Snakemake etc.)
• Proven ability to work independently (solve technical challenges, data analysis, and generate novel research ideas/extract findings)
• Ability to work in multidisciplinary teams and a dynamic research environment
• Excellent scientific writing and communication skills, fluent in written and spoken English.
• At least one first-author publication in a relevant field

The preferred candidate should have an excellent academic track record, solid research experience, and be able to work independently to utilize various strategies to extract novel findings.

Desirable experience and skills

• Familiarity of with registry data sets and longitudinal/time-series data analysis
• Experience with deep generative models (e.g. Variational Autoencoders) and Large
• Experience in genomic data analysis

Workplace and period of employment

The workplace is located at the newly renovated part of the Panum Institute, University of Copenhagen, Denmark. The average weekly working hours are 37 hours per week. The period of employment is 2 years, with the possibility of extension. The starting date is October 1st, 2025, or soon thereafter.

Salary and terms of employment

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 organization. Currently, the monthly salary starts at 38,700 DKK/approx. 5,100 EUR (April 2025 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.

Questions

For further information please contact Associate Professor Jessica Hjaltelin: jessica.hjaltelin@sund.ku.dk You can read more about the HDSAI Section at: www.publichealth.ku.dk/about-the-department/section-for-health-data-science-and-ai/ Foreign applicants may find this link useful: www.ism.ku.dk (International Staff Mobility)

Application

Interested candidates are encouraged to submit an online application by clicking “Apply now” below no later than 6th of June 2025, 23.59pm CET.

Applications must be submitted in English and as one PDF file containing all materials to be given consideration. The application should contain:

• Motivated letter of application (cover letter, max. 2 pages)
• Curriculum Vitae (incl. education, work/research experience, Google scholar link/ORCID ID)
• A certified/signed copy of certificate(s) of education. If the PhD is not completed, a written statement from the supervisor will do.
• Bachelor’s and master’s grades 
• List of publications (highlight three papers especially relevant for this application)
• Contact details for two referees

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