The successful applicant will work in an established laboratory and join a collaborative, vibrant, and growing community of researchers in an environment that values creativity and scientific communications in Łukasiewicz–PORT. In our team, we carry on both wet-lab experiments and dry lab bioinformatics analyses. The candidate will join scientific discussions with other team members. Together with the team leader, the candidate will help with training newcomers. Overall, the candidate will have a great opportunity to participate in research on training and gain knowledge and skills for his/her career development. This position offers the potential for contract extension.

job description

The Quantitative Virology Research Group in Łukasiewicz–PORT invites applications for a postdoctoral position starting as soon as possible. We are recruiting applicants with broad research interests to work on Big Data, multigenomic studies, and computational modeling.

The candidate will join ongoing projects in our team and perform mathematical/computational modeling. The topics include (1) constructing deep learning-based models to predict the molecular microenvironments of the latent HIV reservoir and (2) profiling the landscapes of enriched antisense RNA k-mers across HIV-1 subtypes.

Our laboratory has proposed the presence of distinct molecular microenvironments in the latent HiV reservoir (Chen H. 2026. J Virol 100:e00175-26), which leads to the heterogeneous nature of the reservoir configuration. The candidate will apply an array of “omics” datasets generated in the lab to construct deep learning-based models, enabling the classification and prediction of distinct microenvironments and seek critical predictor attributes in defining distinct microenvironments.

In addition, our lab previously established the streamlined pipeline named the Pathogen Origin Recognition Tool using Enriched K-mers (PORT-EK) (https://github.com/Quantitative-Virology-Research-Group/PORT-EK-version-2), which is a k-mer-based approach, facilitating the comparison of various multigenomic datasets and detecting over-represented genomic regions (i.e., k-mers) linked to specific sequences (Wiśniewski and Chen 2025 IMetaOmics PMID: 41675161; Wiśniewski et al. 2026 BioRxiv DOI:10.64898/2026.02.25.707904). The candidate will further reinforce the pipeline to identify enriched k-mers using HIV-1 antisense transcripts across subtypes. In parallel, the candidate will work on exploring epidemiological models within the framework of phylodynamics and phylogeography and integrating them with population genetics models. Increasing the performance of integration algorithms and model simulations, and developing machine learning methods for analyzing genomic data.