Sort by
Refine Your Search
-
) programme and research school Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures
-
) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
-
vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
-
by using computational models of development to simulate the evolution of evolvability. Main responsibilities The main tasks include: Large-scale simulations of development and evolution under
-
research and methodological development to design and implement novel computational models and solutions. A solid theoretical background and hands-on experience in digital image processing and deep learning
-
and experiences. We regard gender equality and diversity as a strength and an asset. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) is a 12-yr initiative funded with
-
networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
-
rate, and virtually nothing is known about a putative connection between these mutation rates. Using several Drosophila melanogaster model systems, in combination with quantitative genetics, experimental
-
, integrating microfabrication, cell component and biomaterial incorporation, staining of specific biological features, and computational modelling of intrinsic properties. The evaluation of results and further
-
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