Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study
-
-based methods to improve cancer therapy Your profile Qualifications PhD in bioinformatics, computer science, biology, medicine, or mathematical statistics. Experience in cancer research and analyses
-
and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data
-
School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Spatial Biology Our laboratory has recently been awarded an ERC Synergy grant
-
long-lasting adverse health effects in humans and wildlife is also performed. For more information see www.iob.uu.se Data-driven life science (DDLS) uses data, computational methods and artificial
-
at least 1 million DNA barcodes. The project involves collaboration with a computer vision lab at Linköping University, focused on developing AI-assisted techniques for picking out specimens for genome
-
. The research group is part of the National Program for Data-driven Life Science (DDLS), generously funded by the Knut and Alice Wallenberg Foundation: www.scilifelab.se/data-driven/ Our group focuses on studying
-
, especially Bioinformatics, program the applicant must have passed courses within the first and second cycles of at least 90 credits in either, a) Chemistry/Molecular Biology/Biotechnology, or b
-
KTH Royal Institute of Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health Project description Third-cycle subject: Medical Technology (Joint KTH-KI program) In
-
KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science Project description Third-cycle subject: Computer Science This project involves generative modeling