Sort by
Refine Your Search
-
Category
-
Program
-
Employer
-
Field
-
description The project focuses on studying the evolution of evolvability using computational simulations. Evidence from evolutionary developmental biology suggests that evolvability can change rapidly in
-
, computer simulations and various statistical approaches to increase understanding of the ecological and evolutionary processes that underlie speciation and ecological diversification in the context
-
relevant fields of biology (e.g., systems, evolutionary, developmental, computational), physics, mathematics or related fields. In the appointment process, special attention will be given to research skills
-
your application! We are looking for a PhD student in evolutionary genetics interested in contributing to a better understanding of the mechanisms that shape mutation rates. Your work assignments
-
bioinformatics and evolutionary models The project will involve limited amount of experimental testing of computation models. A willingness to engage in this work is required. Experience in practical work
-
will combine state-of-the-art computer vision, modeling and archived specimens to determine biotic and abiotic factors driving spatial variation in molt phenology. It will use museum genomics to recover
-
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
-
development of phylogenetic methods. The EvonetsLab is supported by a Starting Grant from the European Research Council and a DDLS Fellowship from the SciLifeLab and Wallenberg Swedish program for data-driven
-
development of phylogenetic methods. The EvonetsLab is supported by a Starting Grant from the European Research Council and a DDLS Fellowship from the SciLifeLab and Wallenberg Swedish program for data-driven
-
Starting Grant from the European Research Council and a DDLS Fellowship from the SciLifeLab and Wallenberg Swedish program for data-driven life science. The successful candidate will be working within