PhD Positions in Molecular Biotechnology and Data-Driven Life Science KTH Royal Institute of Technology in Sweden

The Molecular Programming group at KTH Royal Institute of Technology, in collaboration with SciLifeLab, is offering two fully funded PhD positions in Stockholm, Sweden. These positions are at the intersection of biotechnology, computation, physics, and machine learning, providing an interdisciplinary research environment that spans molecular biology, DNA nanotechnology, genomics, machine learning, and applied mathematics.

Our group focuses on developing new technologies using DNA as an engineering material. We explore how concepts from computation, network science, and information theory can be translated into molecular systems for sensing, inference, and biological measurement. Research directions include DNA computing, sequencing-based microscopy, molecular networks, spatial transcriptomics, and chemical neural networks.

Position 1: DNA-based chemical neural networks ('smart PCR') involves developing molecular systems where neural-network-like computation is implemented directly through DNA chemistry. The project combines molecular biology, thermodynamics, reaction network design, and computational modelling of DNA strand dynamics. The broader goal is to create programmable biochemical systems capable of recognizing patterns in molecular mixtures without sequencing or conventional digital computation. Suitable backgrounds include biotechnology, molecular biology, biochemistry, engineering, biophysics, or DNA nanotechnology. Computational experience is beneficial.

Position 2: Spatial reconstruction from single-cell genomics focuses on developing graph-theoretical and machine learning methods to recover spatial organization in tissues from single-cell RNA sequencing and molecular network data. The work emphasizes algorithm development, large-scale data analysis, and computational inference for spatial biology. This position is associated with the Swedish national Data-Driven Life Science (DDLS) program initiative. Suitable backgrounds include applied mathematics, physics, computer science, computational biology, engineering, or related quantitative disciplines.

Both positions are fully funded, including stipend and tuition. The research environment is highly interdisciplinary, offering opportunities to collaborate with experts across molecular biology, genomics, machine learning, and applied mathematics. Applicants should have relevant academic backgrounds as described above. Computational skills are advantageous. No explicit GPA or language test requirements are mentioned.

To apply, use the provided Nature Careers application link. Review the group webpage for further project details and prepare your application materials as specified. Submit your application before the deadline.