伦敦玛丽王后大学和新加坡科技局共同培养博士奖学金招一名人工智能无人机方向博士生

雨落云流

伦敦玛丽王后大学和新加坡科技局共同培养博士奖学金招一名人工智能无人机方向博士生。由QMUL的Liu yuanwei教授和Dr Joey Tianyi Zhou共同指导。提供百万全额奖学金可以覆盖英国和新加坡留学所有费用，欢迎大家推荐优秀的学生！

雨落云流

Project Description
With the rapid development of control technology and manufacture business, unmanned aerial vehicles (UAVs), which were originally initiated by the military use, have gradually demonstrated the civil potentials to new applications and markets opportunities, such as emergency communication networks establishment, advanced cargo distribution, aerial photography and video streaming, and wildfire management, to name a few. Regarding communication areas, UAV-aided communication has been recognized as an emerging and promising technique in industry for its superior on flexibility and autonomy. For example, industry projects, such as Google Loon project, Internet-delivery drone in Facebook, and airborne LTE services in AT&T, have been deployed for providing airborne global massive connectivity. To assist 5G communications, promising research scenarios can be as follows: establishing temporal communication infrastructure during natural disasters, offloading traffic for dense networks, and data collection/processing for supporting Internet of Things (IoT) networks. This project aims to invoke the marriage of the AI and the communication for designing the deployment trajectory to establish flexible UAV communication networks.

雨落云流

Qualifications:
All applicants should hold a masters level degree at first /distinction level in Computer Science or Electronic Engineering (or a related discipline). Applicants should have a good knowledge of English and ability to express themselves clearly in both speech and writing. The successful candidate must be strongly motivated for doctoral studies, must have demonstrated the ability to work independently and to perform critical analysis.

Candidates are asked to possess fundamental knowledge and skills in two or more of the following areas:

•        Excellent background in communication theory and signal processing algorithms. Good knowledge of emerging IoT techniques, such as UAV, V2X, wireless caching and mobile computing, etc.
•        Prior experience/education in both theory and practice of machine learning.
•        Hands on experience using one of the following deep learning libraries: Tensorflow, PyTorch, or similar.
•        Good publications on AI or communication is a plus.
•        Strong coding skills. (Python is required and C++ is a strong plus. Comfortable with the Linux environment.)

雨落云流

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