Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
-
Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
-
through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a
-
, machine learning and biophysics. Investigators at CFIN are supported by state-of-the-art research facilities, including MRI, MEG, OPM-MEG, EEG, PET, TMS, eye-tracking and more. CFIN is part of the Danish
-
Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences (geometric deep learning, transformer-based approaches, ...) with a focus on
-
candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of molecular data in cancer genomics. The position is connected
-
Biological Dark Matter (BDM). A main idea of this project is to make use of the pattern matching abilities of the Tsetlin Machine in machine learning to be able to recognize signals in the BDM in
-
& machine learning