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Kennesaw

新虫 (小有名气)

应助: 0 (幼儿园)
金币: 263.3
帖子: 93
在线: 6.9小时
虫号: 29513378
注册: 2022-04-21
专业: 机器人学及机器人技术

[交流] 美国kennesaw state university招智能应用方向博士，全奖，24000美元/年

招生申请截止日期为2023年11月，2024年春季、秋季入学。

待遇：全额奖学金，每年24000美元

招生研究方向：智能应用（Smart Infrastructure）方向博士

申请要求：能熟练使用以下编程语言之一：python，c++，c，有AI 相关经验的学生有优势。
               自觉主动，对新技术有热情。
语言成绩要求： Minimum required score: Internet version (iBT) - 80

导师介绍：Dr. Jian Zhang从事智能机器人、智能应用和无线通信研究，研究成果发表于众多学术顶级杂志和会议（IEEE Transactions on Industrial Electronics，IEEE Internet of Things Journal，IEEE international conference on Industrial technology (ICIT) 等）。导师拥有Mobile robots，6 axis robot arm，以及无人机, RFID reader等科研设备。给学生配备高性能电脑（Precision 3660 Tower， Nvidia GeForce RTX4090, 3660T）用于AI 训练，和其他实验室共享boston dynamics Spot （机器狗）。导师拥有6年科技大厂工作经历，其中四年位于manager 位置，管理过40多人工程师团队。目前仍与诸多企业（Walmart, Delta Airline,Fedex）有合作。学生除得到导师学术相关指导，还可以学到相关项目管理经验，之前毕业的phd 已经开始走上管理岗位。

导师链接：https://facultyweb.kennesaw.edu/jzhang51/index.php

PHD项目链接： https://engineering.kennesaw.edu ... linary-engineering/

预计参与项目链接： https://facultyweb.kennesaw.edu/jzhang51/research.php  （When RFID and AI meets for Occluded Body Skeletal Posture Capture in Health Care）

学校简介：肯尼索大学-玛丽埃塔校区（Kennesaw State University - Marietta Campus）地址在1100 South Marietta Pkwy SE, Marietta,GA，肯尼索大学的工程和计算机相关专业全部都在玛丽埃塔校区。玛丽埃塔校区毗邻亚特兰大，交通便利，社区安全，周围大厂云集、找工作便利，导师与诸多大厂有合作。

联系方式：
有意者请将简历发送至导师邮箱 jzhang51@kennesaw.edu  ，邮件标题为“申请者姓名-博士申请”，简历内容中英文皆可。

Ph.D. candidate opening

Position description:  The prospective Ph.D. student will focus on research in the cutting-edge Internet of Things (IoT), more specifically to develop a low-cost, unobtrusive, and robust posture capture system by using Radio-Frequency Identification (RFID) technique.  Ultra-High Frequency (UHF) RFID tags will be fabricated into fitness clothing, and RFID readers will be deployed to collect observations from the tags. The precise position of each tag can be obtained by our proposed RFID localization method. Subsequently, the posture and motion of the body can be reconstructed by registering tags to a skeletal model with a deep learning method. The proposed system can be used in clinical and home environments, especially for non-visible scenarios (e.g., clothing and/or bedding occlusions), for 1) Preventing and detecting elders at high risk for falls or wandering, 2) Detecting abnormal body movements, especially for those associated with sleep disorders and Parkinson’s disease, and 3) Enhancing training and evaluation processes in rehabilitation settings.

Requirements of the ideal candidate:
Self-motivated and have a passion for new technology and research;
Experiences in Python programming  and it better is also can program in C++/C;
Experiences in AI/Deep Learning is a plus;
Basic knowledge of wireless communication;
Good communication skill(oral and writing) in English.

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