Sort by
Refine Your Search
-
expertise in these areas is highly encouraged. The selected candidate will work on cutting edge technologies in an excellent research environment, with a potential to work with a Quantum Computer through our
-
models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations and/or experimental
-
for physical systems. The postdoc will work on projects focusing on one or more of the following: Robot Learning & Autonomy – Developing algorithms that allow robots to learn (via exploration or imitation) from
-
, telecommunications or related field. Other requirements include: Strong background in communication theory, signal processing, wireless/optical communications, and communication system engineering Extensive experience
-
the connections between accretion and ejection. The processes involved in triggering outbursts, using optical monitoring and the real-time pipeline X-ray Binary New Early Warning System (XB-NEWS) developed at NYUAD
-
Description The Center for Artificial Intelligence and Robotics (CAIR) at New York University Abu Dhabi invites applicants to apply for the open Postdoc position in the Project Collaborative Multi
-
experience in physiological signal processing (e.g., EMG, EEG, ECG) is an advantage. Familiarity with HCI principles and frameworks, in particular, experience conducting usability studies and designing user
-
, statistical signal processing, optimization theory, machine learning and artificial intelligence. The candidate is expected to actively participate in experimental work focused on building datasets of channel
-
, the candidate will find many international experts and postdocs with whom to interact. Weekly seminars are in place across the various research areas represented at NYUAD. The successful applicant will also
-
Strong background in communication theory, signal processing, and wireless communications, Extensive experience in physical (PHY) layer algorithm design and performance analysis, Proven track record