Sort by
Refine Your Search
-
Listed
-
Country
- United States
- United Kingdom
- Sweden
- Germany
- France
- Singapore
- Spain
- China
- Belgium
- Italy
- Austria
- Netherlands
- Canada
- Portugal
- Denmark
- Australia
- United Arab Emirates
- Poland
- Lithuania
- Hong Kong
- India
- Japan
- Romania
- Switzerland
- Czech
- Greece
- Ireland
- Luxembourg
- South Africa
- Andorra
- Cyprus
- Finland
- Saudi Arabia
- Armenia
- Barbados
- Europe
- Latvia
- Malta
- Mexico
- Morocco
- Norway
- Taiwan
- 32 more »
- « less
-
Field
- Computer Science
- Medical Sciences
- Economics
- Engineering
- Biology
- Science
- Business
- Psychology
- Education
- Mathematics
- Social Sciences
- Materials Science
- Arts and Literature
- Sports and Recreation
- Chemistry
- Linguistics
- Humanities
- Environment
- Law
- Earth Sciences
- Electrical Engineering
- Physics
- Philosophy
- Design
- 14 more »
- « less
-
The VCC center at KAUST is looking for research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep learning. A suitable candidate
-
workloads including embedding generation, LLM inference, and cognitive search. Develop Snowpark Python transformations, UDFs, and machine-learning features. Implement vectorized storage, model-serving
-
for AI and Machine Learning included as well as industrial statistics), which will complement our current research portfolio (see https://stat.kaust.edu.sa) and have a research profile that can potentially
-
extraction. 2. Be responsible for the application of AI and machine learning techniques to improve tissue image interpretation, for use in case selection and tissue annotation for tissue microarray
-
project “Analytics for Learning with Machines” (ALMA) The position is TV-L E13, 75%, limited to 3 years, funded by the Deutsche Forschungsgemeinschaft (DFG). The project is a Franco-German collaboration
-
learning workflows, and perform data quality control across multiple datasets. The ideal candidate will implement data science analytical models and machine learning models following established
-
leverage state of the art machine learning models (AlphaFold2, RFdiffusion) and multi-omics data integration to guide the rational design and optimization of therapeutic antibodies. Overall, you will have
-
the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
-
of interest include, but are not limited to, stochastic, discrete, large-scale, and data-driven optimization, machine learning methods for sequential decision making, or stochastic modeling and prescriptive
-
architectures for TTS and ASR Entrenamiento de modelos a gran escala utilizando frameworks modernos de deep learning / Training large-scale models using modern deep learning frameworks Publicaciones en