Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
-
In this project, the selected candidate will join us in conducting research in statistical learning, developing data-driven methods to learn models of large-scale signals and systems from data
-
of algorithms, data structures, high-performance computing, machine learning and microbiology. The position at the Department of Molecular Biology at Umeå University is temporary for four years to start
-
. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
-
. The project is highly interdisciplinary and will provide training in clinical microbiology, infection epidemiology, machine learning, data harmonization, and data science. You will also participate in DDLS
-
, and registry-linked outcome data. In this project, you will develop and apply AI-based methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen
-
of ion conductivity in complex battery materials on a large scale. Model‑generated data will be used to identify key relationships between material structure and ionic conductivity through advanced data
-
their PhD. Project description The aim of this project is to deepen the fundamental understanding of machine learning through the lens of optimal transport theory, systems theory, and statistical physics
-
precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related
-
student in this project, you will contribute to the development of new models and methods in machine learning for D-MIMO integrated sensing. This includes working with large amounts of data generated by a