Sort by
Refine Your Search
-
and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create
-
position is part of a five-year research program funded by the Wallenberg Foundation, which aims to develop and apply computational tools to understand the evolution of biodiversity (see https
-
to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming
-
at the Wallenberg Laboratory. The group is part of the national Data-Driven Life Science (DDLS) program, funded by the Knut and Alice Wallenberg Foundation. Their research focuses on developing computational methods
-
publications in relevant research area. Candidates must be fluent in spoken and written English and highly motivated. Candidates are expected to pursue and plan research independently, but also to function in a
-
degree in relevant fields (bioinformatics, immunology, computational biology, mathematics, and/or statistics). Strong programming skills in R and/or Python Demonstrated strong ability in analyzing high
-
to teach in Swedish within three years, and if necessary, a language plan will be created in connection with the appointment as support. Eligibility Those eligible to be employed as associate professor
-
Proficient in Python Good knowledge of database modeling Excellent communication skills in English Preferred Qualifications Knowledge of Ruby programming Experience of both frontend and backend development
-
) 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
-
and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data