Computational Postdoc in Ribosome Specialization at the Biotech Research and Innovation Centre
Pubblicato il 08-09-2026 - Department of Pharmacy, University of Copenhagen in Pallare
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Tutte le competenze, le qualifiche e l'esperienza necessarie per un candidato di successo sono elencate nella seguente descrizione.
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Computational Postdoc in Ribosome Specialization at the Biotech Research and Innovation Centre
Our research
Our team aims to understand how ribosome specialization leads to the instalment of translational programs and how these processes are hijacked in human disease. Due to the central role of the ribosome in many biological processes, we use a broad panel of model systems, including human stem cells, neuronal cultures, and 2D and 3D cultures of human cells and patient samples.
The team is highly international, and we have a broad range of local and international collaborators. We believe that cooperation and collegiality are essential elements for research successes. You can find more information about the team and our work here: project
You will join the Lund Group focusing on ribosome heterogeneity and specialized translation.
We use a broad array of wet lab and computational techniques and develop new techniques to push our research forward. We highly value collaboration and cocreation and you will have freedom to codevelop the project.
Your project will mainly focus on developing computational tools to analyse single molecule nanopore sequencing data with a focus on RNA modifications. Key methods in nanopore sequencing and computational modelling have been established in the group. The project is conducted in collaboration with the deep probabilistic programming group of Thomas Hamelryck: persons/271052
Profile
We are looking for a highly motivated and enthusiastic scientist with the following competencies and experience:
Essential Experience And Skills
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- You have a PhD in biomedical or natural sciences
- Strong foundation in bioinformatics and machine learning, including deep learning
- Experience training and running models on shared HPC clusters and remote GPU servers, including working