-
higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
-
science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and
-
analysis to better understand how molecular and cellular processes are coordinated across cells, tissues, and organ systems in human health and disease. You will be responsible for developing and applying
-
insights on long-standing questions on sex chromosome evolution and on evolutionary processes at large. Duties In this PhD project, you will explore some of the most fascinating questions in evolutionary
-
for the position: Experience with mammalian cell culture and associated techniques. Experience in high throughput sequencing. Experience in custom processing of high throughput sequencing data. Experience in
-
is part of the national research programme DDLS. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels
-
(e.g., Snakemake, Nextflow) and reproducible data processing pipelines. Knowledge of transcriptomics and alternative splicing analysis, including isoform-level quantification tools. Programming skills in
-
About the opportunity: Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and
-
processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims
-
data-driven diagnostics. About the project Cancer treatment often involves surgery, chemotherapy, and radiotherapy. While these treatments have improved outcomes, they can be long-lasting and cause