15 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of Lund
Sort by
Refine Your Search
-
; significant practical experience in 3D image analysis or computer vision; knowledge and experience in scientific programming (python (preferred), Matlab or other relevant language) with application to image
-
of mathematical areas. The position will be placed at the Department of Computer Vision and Machine Learning (CVML) at the Mathematics Centre (https://maths.lu.se/). Mathematics Centre is a department
-
(PhD students, post-docs, researchers and teachers) and is located at the BMC-building (Biomedical Center). The expertise includes computational and experimental mechanics of biological tissues, where a
-
Institute of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work may include clinical and biomedical projects. It may
-
for the position are: It is meritorius if you have solid experience with the following computer programs: CryoSPARC, Phenix, Coot, Chimera and Pymol, It is meritorius if you have experience working with eukaryotic
-
, bacteriophages. Prof. Hauryliuk obtained his PhD in 2008 at Uppsala University, Sweden. His scientific contributions were recognized though the Ragnar Söderberg fellowship in Medicine (2014), the Swedish Fernström
-
of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. For more information about the total announced post-doctoral positions within
-
eco-evolutionary physiology and thermal biology. Work duties The position is linked to the research program "HotLife – Pathways to Survival in a Warmer World," funded by the European Research Council
-
forests and marine environment and pest surveillance in aquafarming. Our group will comprise a handful of PhD candidates, and several researchers and MSc students and also a broad interdisciplinary network
-
environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman