Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Portugal
- Sweden
- France
- Netherlands
- Spain
- Singapore
- Norway
- Italy
- Belgium
- Denmark
- Poland
- United Arab Emirates
- Australia
- Austria
- Luxembourg
- Finland
- Romania
- Canada
- China
- Morocco
- Worldwide
- Ireland
- Switzerland
- Estonia
- Hong Kong
- Malta
- Greece
- Japan
- Brazil
- India
- Lithuania
- Saudi Arabia
- Andorra
- Armenia
- Cyprus
- Czech
- Europe
- New Zealand
- Taiwan
- 32 more »
- « less
-
Program
-
Field
- Computer Science
- Engineering
- Medical Sciences
- Biology
- Economics
- Mathematics
- Science
- Chemistry
- Materials Science
- Earth Sciences
- Electrical Engineering
- Linguistics
- Business
- Physics
- Psychology
- Environment
- Humanities
- Philosophy
- Arts and Literature
- Education
- Law
- Social Sciences
- Sports and Recreation
- 13 more »
- « less
-
candidates will be required to fund the difference between the home fees and international fees. Overview Are you interested in developing Brain-Computer Interface technologies to control brain stimulation in
-
algorithms for behavioral cue extraction and novel approaches for the modeling people interaction, with application to medical research and affective computing. Responsibilities: Write code and develop novel
-
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
-
methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work
-
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
-
. 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
-
Prof. Neil Walton (Durham University, UK). The general aim of this project is to develop throughput-optimal entanglement distribution algorithms (both centralized and decentralized algorithms
-
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
-
, 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
-
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