Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
, molecular communication, and systemic molecular networks, and to identify how these processes change across physiological and disease states. Through this work, you will develop predictive and biologically
-
higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
-
Medicine (WCMTM) and SciLifeLab in the University of Gothenburg. and is tightly connected to a broad network of international collaborators. Duties Develop and perform high-resolution spatial transcriptomics
-
more about Doctoral studies (PhD) | KTH | Sweden . Union representatives Contact information for union representatives. Doctoral Student’s network (Students’ union on KTH Royal Institute of Technology
-
. Doctoral Student’s network (Students’ union on KTH Royal Institute of Technology) Contact information for PhD chapter . To apply for the position Apply for the position and admission through KTH’s
-
these programs will facilitate the group members’ nationwide networks and multidisciplinary collaborations. Working in a young team, group members will benefit from first-hand guidance from the principal
-
of molecular networks in cancer to advance precision medicine. By integrating high-throughput data (e.g. transcriptomics, proteomics, metabolomics) with prior knowledge of molecular interactions, we construct
-
broad national network of future academic leaders within SciLifeLab and Wallenberg’s national program for data-driven life science (DDLS fellows) and the fellow programs at the Wallenberg Centers
-
training to the Swedish life science research community, with a strong national and international collaborative network. The main responsibilities are to: Carry out advanced data analyses within nationally
-
multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta