Chiesi Group, in collaboration with the DDLS Research School, invites applications for two postdoctoral positions in computational biology and precision medicine, based in Stockholm, Sweden. These positions are part of the DDLS Research School Postdoc program, a national initiative funded by the Knut and Alice Wallenberg Foundation, aiming to advance data-driven life science in Sweden.

The successful candidates will join a dynamic team at Chiesi Nordic, working at the intersection of computational biology, translational medicine, and machine learning. The research focuses on developing and applying novel computational frameworks for patient endotyping and biomarker discovery in chronic respiratory diseases such as asthma, COPD, and bronchiectasis. Projects involve leveraging large-scale multi-omic and multimodal data from patient registries and clinical trials, with key topics including graph and deep learning of molecular biomarkers and the integration of imaging and molecular data for precision medicine.

Collaborations include Prof Muhammad Arif at University of Gothenburg and Prof Golnaz Taheri at KTH Royal Institute of Technology. The DDLS Research School provides a national framework for training, networking, and career development, connecting postdoctoral researchers and PhD students across Sweden. The program supports research in four strategic areas: Cell and Molecular Biology, Evolution and Biodiversity, Precision Medicine and Diagnostics, and Epidemiology and Biology of Infection.

Applicants must hold a doctoral degree (or equivalent) prior to starting, preferably obtained within the last three years, and have experience in systems biology, machine learning, or related fields. The program offers fully funded 2-year positions with a total of 2 million SEK in funding per project. Candidates are expected to participate in DDLS Research School activities and contribute to the national DDLS community.

Application deadline is March 31, 2026. For more information and to apply, visit the DDLS Research School website or contact the supervisors directly. This is a unique opportunity to contribute to cutting-edge research with real-world impact in data-driven life science.