Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
- Computer Science
- Engineering
- Medical Sciences
- Biology
- Economics
- Science
- Materials Science
- Mathematics
- Business
- Earth Sciences
- Chemistry
- Linguistics
- Electrical Engineering
- Environment
- Social Sciences
- Arts and Literature
- Education
- Law
- Physics
- Psychology
- Philosophy
- Humanities
- Sports and Recreation
- 13 more »
- « less
-
, including device protection, impedance control and EMI mitigation; architect low‑inductance current paths, gate‑drive schemes (e.g., GaN/Si MOSFET drivers), and snubbers tailored to OLED stack constraints and
-
recognized for the work you do, and enjoy the unique value the CSUN community can offer. If this sounds like you, you’ve come to the right place. Learn more: https://www.csun.edu/about-csun . Major Duties
-
. However, in many real-world and latency-critical applications, performance cannot be assessed solely through final recognition accuracy. Instead, the value of a prediction strongly depends on its timeliness
-
Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
-
opportunity to work in a top-tier interdisciplinary setting. This is what you will do You will develop predictive computational models to capture the formation and heterogeneous structure of microthrombi, with
-
-scale transport, energy, defence, and technology initiatives, there is a critical need for new AI-enabled approaches to understand, predict, and improve the behaviour of these multi-billion-dollar
-
open to candidates with a strong interest in either: i) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane
-
field. This approach is related to data assimilation, allowing for better prediction, control, and optimisation of turbulent systems in engineering, energy, and environmental applications
-
research fellows to join a multi-year research initiative sponsored by the Bezos Earth Fund . This project aims to develop and deploy advanced AI-driven learning, prediction, and decision-making tools
-
broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data