Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Sweden
- United Kingdom
- Germany
- France
- Norway
- Portugal
- Singapore
- Belgium
- Netherlands
- Spain
- Denmark
- China
- Australia
- Italy
- Canada
- Luxembourg
- United Arab Emirates
- Hong Kong
- Switzerland
- Austria
- Czech
- Finland
- Morocco
- Ireland
- Poland
- Cyprus
- Japan
- Brazil
- Latvia
- India
- Saudi Arabia
- Lithuania
- Bulgaria
- Taiwan
- Andorra
- Iceland
- Israel
- Slovakia
- Slovenia
- South Africa
- Armenia
- Barbados
- Estonia
- Europe
- Greece
- Malta
- Mexico
- New Zealand
- Vietnam
- 40 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Economics
- Engineering
- Biology
- Science
- Business
- Education
- Psychology
- Mathematics
- Materials Science
- Social Sciences
- Arts and Literature
- Humanities
- Chemistry
- Linguistics
- Earth Sciences
- Environment
- Law
- Electrical Engineering
- Sports and Recreation
- Physics
- Philosophy
- Design
- 14 more »
- « less
-
(e.g. computer vision, deep learning, AI) and green life sciences (e.g., remote sensing, crop modelling, and food security), within the European funded project AgriscienceFM (Horizon programme), which
-
to join an advanced research project at the intersection of quantum computing and machine learning, focused on developing scalable and coherent training methods for quantum models. This project tackles
-
of innovative multicriteria models for the management, integration, and enhancement of geological and geophysical data, using geostatistical, geomatic, and, where appropriate, machine learning techniques
-
intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
-
applying machine learning and computational methods for protein design, in close integration with experimental enzymology and biocatalysis. The tasks include: Development and application of AI and machine
-
) to develop accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models
-
Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
-
. Candidates with experience in dimension reduction, deep learning, machine learning, modeling neuroimaging data are especially encouraged to apply. Excellent written and communication skills are required
-
(EMG), to capture detailed motion, interaction forces, and muscle activity. Predictive Physiological Modeling: Development of machine learning models capable of anticipating motion intent while
-
strong publication record (first-author papers in high-impact journals preferred). Demonstrated expertise in at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual