Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
of Computing Science , Mathematics and Mathematical Statistics , Molecular Biology , Plant Physiology and Clinical Microbiology . More information regarding accessible research infrastructure can be found here
-
different aspects of evolvability – the ability of organisms to evolve. We are interested in developing computational and mathematical tools to understand and quantify evolvability while exploring its
-
are required: You have graduated at Master’s level in bioinformatics, computational biology, data analysis, computer science, biostatistics, biological engineering, applied mathematics/physics or completed
-
mathematical statistics (University of Gothenburg / Chalmers University of Technology) Prof. Mats Nilsson, pioneer in spatial genomics (SciLifeLab & Stockholm University) Integration into the national DDLS
-
machine learning, engineering, data sciences, applied mathematics, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including
-
. To meet the general entry requirements for doctoral studies, you must: Hold a Master’s degree in computer science, image analysis and machine learning, engineering, data sciences, applied mathematics
-
background in biology, programming or mathematics is meritorious. Knowledge in medical image processing, image registration, and large-scale analyses of genetic (including Mendelian randomization), protein, or
-
at developing and applying computational tools to understand the evolution of biodiversity (see https://islandevolution.github.io/ ). As a postdoc in this project, you will work with mathematical modelling
-
in computer science, engineering, data sciences, applied mathematics, machine learning, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at
-
science. We are looking for a candidate with a PhD in either engineering/computer science/physics/mathematics. Experience with ML implementation (ideally interpretable ML and/or generative AI) is required