Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Portugal
- Sweden
- France
- Netherlands
- Spain
- Singapore
- Italy
- Norway
- Belgium
- Denmark
- Poland
- United Arab Emirates
- Australia
- Austria
- Finland
- Luxembourg
- Romania
- China
- Morocco
- Canada
- Ireland
- Worldwide
- Switzerland
- Estonia
- Hong Kong
- Japan
- Malta
- Greece
- Andorra
- Brazil
- India
- Lithuania
- Armenia
- Cyprus
- Czech
- Europe
- New Zealand
- Saudi Arabia
- Taiwan
- 32 more »
- « less
-
Program
-
Field
- Computer Science
- Engineering
- Medical Sciences
- Biology
- Economics
- Mathematics
- Science
- Materials Science
- Chemistry
- Earth Sciences
- Electrical Engineering
- Linguistics
- Business
- Physics
- Psychology
- Environment
- Humanities
- Philosophy
- Arts and Literature
- Education
- Law
- Social Sciences
- Sports and Recreation
- 13 more »
- « less
-
imaging (MRI). The Computational Biomedical Imaging Group (CBIG) pursues research on the development of new algorithms for the reconstruction and post-processing of medical and biological images. Active
-
. You can continue your career journey with us! The Slomka Laboratory focuses on developing innovative methods for fully automated analysis of nuclear cardiology data using novel algorithms and machine
-
project team regularly use for the production of model colloidal films, ceramic dielectrics, photovoltaics and battery electrodes to provide the datasets required to educate the machine learning algorithms
-
will provide PhD training to 15 Doctoral candidates (DCs). Consortium objectives: MetaTune aims to develop a new generation of reconfigurable metasurfaces that enable efficient, simple, and industry
-
, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
-
language models to whole genome sequencing data - Develop algorithms and neural network architectures for the prediction of structured outputs (i.e. trees, graphs) - Implement and develop methods
-
. The ultimate goal is to develop theory and methods for the construction of low-complexity invariant sets, using computationally tractable algorithms. Funding Notes This is a self-funded research project. We
-
areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research
-
formula is true or false (EXPTIME vs NP). Can we develop and implement efficient algorithms for this problem? This problem has been attacked using multiple different methods for the past 40 years, without
-
schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . To develop novel inertial microfluidics and impedance-based cell sorter. To develop novel label-free CQAs based