Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Sweden
- United Kingdom
- France
- Germany
- Norway
- United Arab Emirates
- Canada
- Spain
- Netherlands
- Denmark
- China
- Hong Kong
- Poland
- Belgium
- Finland
- Ireland
- Switzerland
- Luxembourg
- Singapore
- Australia
- Austria
- Japan
- Morocco
- Italy
- Saudi Arabia
- Taiwan
- Czech
- Portugal
- Romania
- Andorra
- Brazil
- Cyprus
- Greece
- India
- New Zealand
- South Africa
- Barbados
- Estonia
- Europe
- Lithuania
- Mexico
- Slovenia
- Vietnam
- Worldwide
- 35 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Biology
- Engineering
- Science
- Economics
- Mathematics
- Chemistry
- Psychology
- Education
- Earth Sciences
- Humanities
- Materials Science
- Environment
- Physics
- Law
- Social Sciences
- Arts and Literature
- Electrical Engineering
- Linguistics
- Business
- Statistics
- Philosophy
- Sports and Recreation
- 14 more »
- « less
-
as an integrated analytical framework, we apply comparative risk assessment, disease modeling, machine learning, and survival extrapolation methods to systematically quantify the long-term impacts
-
, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7
-
inequalities and Sobolev-type spaces (with Hytönen and/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic
-
of Mechanical and Aerospace Engineering at UCSD invites applications for a postdoctoral fellowship funded by the Gordon and Betty Moore Foundation focused on the topic of machine learning-based ocean state
-
computers lack such abilities. The goal of the Adaptive Bayesian Intelligence Team is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with
-
capable of understanding, learning, and acting in complex, dynamic settings. The lab’s work lies at the intersection of computer vision, multimodal learning, and robotics, advancing next-generation embodied
-
computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
-
, machine learning or AI to computational modeling, simulations, and advanced data analytics for scientific discovery in materials science, biology, astronomy, environmental science, energy, particle physics
-
Professor John Harlim and his collaborators, Yan Li in the Electrical Engineering department, and Daning Huang in the Aerospace Engineering department in the area of Scientific Machine Learning. The project
-
support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating