Sort by
Refine Your Search
-
Data Driven Life Science (DDLS). About the DDLS Fellows program Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes
-
550 million, out of which about two thirds derive from external funding. IGP has approximately 400 employees, out of which 100 are PhD-students, and there are in total more than 850 affiliated staff
-
-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
-
of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral
-
program 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 cellular processes
-
University. As a doctoral student, you will be trained in a scientific approach. In short, you will be trained to think critically and analytically, to solve problems independently using the right methods, and
-
, we are announcing the position as DDLS PhD student in Data driven cell and molecular biology covers research that fundamentally transforms our knowledge about how cells function by peering
-
communicate on a daily basis with the Head of Unit and Lab Manager. You will also actively participate in technology development with regards to analytical methods and application of workflows to user projects
-
methods. Good computer skills. Meriting Qualifications Documented theoretical or practical experience in structural biology and/or mass spectrometry. Experience in project management and communication with
-
multiomics across diverse biological systems, including animal and plant tissues. The position offers the opportunity to contribute to method development and experimental workflows in a collaborative and