Sort by
Refine Your Search
-
Listed
-
Country
- United States
- Australia
- Singapore
- United Kingdom
- Austria
- France
- Spain
- Sweden
- Germany
- Belgium
- Italy
- Canada
- Denmark
- India
- United Arab Emirates
- China
- Ireland
- Netherlands
- Hong Kong
- New Zealand
- Poland
- Switzerland
- Cyprus
- Japan
- Lithuania
- Portugal
- Bulgaria
- Czech
- Finland
- Luxembourg
- Morocco
- Norway
- Saudi Arabia
- South Africa
- Andorra
- Armenia
- Barbados
- Europe
- Fiji
- Greece
- Kyrgyzstan
- Romania
- Slovenia
- Taiwan
- 34 more »
- « less
-
Field
- Computer Science
- Economics
- Medical Sciences
- Business
- Science
- Engineering
- Education
- Biology
- Arts and Literature
- Mathematics
- Law
- Social Sciences
- Materials Science
- Environment
- Sports and Recreation
- Humanities
- Linguistics
- Chemistry
- Psychology
- Philosophy
- Earth Sciences
- Electrical Engineering
- Design
- Physics
- Statistics
- 15 more »
- « less
-
Wee Kim Wee School of Communication and Information Nanyang Technological University, Singapore Part-Time Lecturer for IN6242 Deep Learning Foundations We are seeking a part time lecturer to teach
-
Posting Details Position Information Fiscal Year 2025-2026 Position Title Associate Director, Deep Tech Incubation Programs Classification Title Associate Director Department Business and
-
settings. The ultimate goal is to enable early, systematic, and robust screening of children at risk of neurodevelopmental disorders. Deep learning models typically produce point predictions, whose
-
(Renforcement Learning, RL) : des travaux récents montrent des gains avec Q learning [3] et Deep Q Network (DQN) [4] pour le routage d'intrication, mais en contrôleur central avec vue globale de l'état, ce qui ne
-
conditions (Burgard et al., 2022). The application of deep learning to this problem has yielded promising results (Rosier et al., 2023; Burgard et al., 2023). Further development and refinement
-
multisensor fusion and ondevice AI pipelines that guarantee tight latency, power efficiency, and fail-safe robustness. Driving hardware–software codesign to radically optimize state estimation and deep-learning
-
will design and validate advanced multi-agent Deep Reinforcement Learning (DRL) and/or Digital Twin (DT)-enabled methods for efficient, scalable and time-critical handover optimisation. The work will
-
basic experimental design. Hands-on experience with classical machine learning methods such as linear/logistic regression, decision trees, and gradient boosting. Familiarity with deep learning concepts
-
. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------...
-
[Pichon 2024] or infra-red [Abdel-Khalek et al 2021], connected in IoT, inexpensive for wide distribution. Combined with artificial intelligence methods, in particular deep learning algorithms, we obtain