Sort by
Refine Your Search
-
an outstanding and ambitious postdoctoral researcher in computational biology to pioneer understanding and modeling of tissue architecture using single-cell and spatial transcriptomics data. The focus will be
-
to interact and communicate efficiently with collaborators, and publish internationally. It is important for us to keep our lab and instruments clean and organized, as a postdoctoral researcher in the group you
-
, chloroplast biology, or protease biology Ability to work independently and collaboratively in an international research environment, with a strong drive to take ownership of the project and develop their own
-
basic research with clinical applications. This position is recruited within Patrick Sandoz’s newly established research group at the Division of Biomedical Engineering. As an early member of the group
-
. Approximately 40 people work within the Department. You will be part of a research group in molecular evolution that focuses on studies and development of antibodies in health and disease. Subject description The
-
, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
-
of computational tools and software for cancer risk prediction. This position offers the chance to engage in cutting‑edge interdisciplinary research at the intersection of ML and cancer research, and to contribute
-
is full-time for 2 years, with access starting in May 2026 or by agreement. Departmental specific information The research will be carried out in the laboratory of Cemal Erdem at the Department
-
School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Job description The research group headed by Emma Lundberg is connected to KTH and located at SciLifeLab in Stockholm
-
recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches