Sort by
Refine Your Search
-
interdisciplinary and to learn new skills and to perform research in collaboration with others. We seek candidates with the following qualifications: A doctoral degree in a Bioscience-related field awarded
-
include absence due to illness, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice or other forms of appointment
-
clinical service, appointments of trust in trade union organizations, or similar circumstances. Doctoral degree should be within bioinformatics, machine-learning, computational biology, genomics, or a
-
on computer networking and distributed systems, computer security and privacy, and software engineering. Both research and education are conducted in close cooperation with international, national, and regional
-
sequencing, and with computer scientists at KTH in Stockholm, focused on developing scalable probabilistic machine learning techniques for online phylogenomic analysis and placement of DNA barcodes. You will
-
The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
-
systems, and machine learning. While the initial focus of the position is on this project, we offer significant opportunity for the applicant to develop their own independent research trajectory in
-
great advantage: Forest and wood production processes Wood construction Furniture manufacturing Wood material science Machine learning Process simulation and optimisation The postdoctoral fellow is part
-
and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
-
application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description