爱尔兰都柏林大学（QS 126）招两名全奖博士生，EE/CS，深度学习模型压缩加速 软/硬件

帮老师招生啦
爱尔兰都柏林大学(University College Dublin,  UCD, QS排名126）  Dr. Deepu John助理教授招收两位全奖博士生，资助2.2万欧/年，EE系，与CS交叉，研究方向为:

• 深度学习模型压缩加速（软件方向)
• IOT部署加速（FPGA/ASIC/硬件方向)

各位方向相符或者希望从事类似方向的师弟师妹可以大胆投递（详见下面两则广告）。


导师主页：[url=https://people.ucd.ie/deepu.john/about]https://people.ucd.ie/deepu.john/about[/i][/u][/url]
导师邮箱：deepu.john@ucd.ie


Application open: Immediate, Position will be closed as soon as suitable candidate is identified  
推荐理由：

• Dr.  Deepu John人非常nice，实验室氛围轻松自由、有学术活力，帮助并鼓励学生按时、尽早毕业；
• 爱尔兰是英语国家、欧洲硅谷，环境很安全，IT发达，各国际大厂都在爱尔兰有分部；
• 博士毕业后做两年博后即可按Critical Skills拿Stamp4永居。
• UCD国际上排名靠前，每年都有国内的985、211院校过去开教职招聘宣讲会；
• UCD与北京工业大学有合作办学，因此每年都有包来回机票回国做短期助教的机会，工资另付。

总的来说，非常推荐大家投递，欧洲很佛系，申请难度比国内名校小，留国外/回国、去学术界/工业界前景都很好，4年读完还能攒下一大笔钱。


How to Apply
The project will be supervised by Dr. Deepu John. Applications consisting of a cover letter, curriculum vitae, degree transcripts should be sent to deepu.john@ucd.ie  with subject line PhD Application 2024 . Only shortlisted candidates will be contacted.






PhD Research Scholarship in Dynamic Model Compression for Edge AI
Applications are invited for a fully funded 4-year PhD position at UCD, Ireland.
Background
This project will investigate advanced techniques for compressing machine learning models and developing dynamic neural networks tailored for edge AI applications. The aim is to optimize the performance and efficiency of complex models, enabling their deployment in resource-constrained environments such as IoT devices enabling TinyML.
Traditional machine learning models often require substantial computational resources, power etc posing challenges for deployment in edge devices. This project seeks to overcome these challenges by creating methods for dynamically adaptable machine learning, ensuring good performance while consuming minimal resources and power.
 
Key Objectives

• Develop and implement novel model compression      techniques capable of dynamic adaption.
• Design dynamic neural networks that can adapt      their structure and parameters based on available computational resources.
• Validate these techniques in real-world embedded,      edge AI systems, such as IoT devices.

 
The project will involve collaboration with multi-disciplinary researchers who are working on RISC-V based SoC design, AI accelerator design, IoT Sensing etc. The position also provides the opportunity to gain valuable experience in design/testing of embedded edge systems. The successful candidate will get opportunities to present their work in international conferences and workshops, and publication of the research in high impact journals.
 
Applicants for this position should ideally have:
·Bachelors/Master’s degree in: Electrical /Computer Engineering or equivalent.
·Prior experience in machine learning and model compression techniques.
·Experience with dynamic neural networks, embedded systems, edge AI
·Strong analytical and programming skills in Python/Matlab and/or embedded C/C++
·Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch)
·Good interpersonal and English language skills (IELTS/ToEFL or willing to take)
·Be enthusiastic and willing to learn new concepts and ideas.
Application open: Immediate, Position will be closed as soon as suitable candidate is identified


How to Apply
The project will be supervised by Dr. Deepu John. Applications consisting of a cover letter, curriculum vitae, degree transcripts should be sent to deepu.john@ucd.ie with subject line PhD Application 2024 . Only shortlisted candidates will be contacted.


PhD Research Scholarship in Low-Power Deep Neural Networks for Edge Sensors
Applications are invited for a fully funded 4-year PhD position at UCD, Ireland.
Background
This project will investigate novel Deep Neural Network (DNN) hardware accelerators for deployment in IoT edge devices. The project aims to address the challenges of implementing complex DNN models on energy efficient and performance driven IoT Sensors. DNN accelerators that are scalable, and easily adaptable to different AI models and applications are our focus. The DNN accelerators developed will be demonstrated in hardware for a wearable healthcare application such as heartbeat classification.
 
In traditional systems, the data generated in IoT devices are sent to cloud servers over wireless networks. AI and deep learning techniques are employed on servers for data processing and analytics. Due to the large amount of data generated in these sensors, it is beneficial (for system power, bandwidth) to process this data locally at the IoT node and send the inference to the cloud instead. In this project, we plan to develop RISC-V CPU based DNN accelerators that are programmable and plan to verify it on an FPGA/ASIC. For reducing the complexity of DNN hardware, we plan to use a combination of algorithmic model compression techniques and circuit approaches such as 1) optimizing the topology 2) applying pruning /quantization techniques 3) optimizing accelerator memory access schemes etc.
 
The project will involve collaboration with multi-disciplinary researchers who are working on low power circuit design, machine learning and data analytics etc. The position also provides the opportunity to gain valuable experience in design, fabrication and testing high performance systems. The successful candidate will get opportunities to present their work in international conferences and workshops, and publication of the research in high impact journals.
 
Applicants for this position should ideally have:
  ·Bachelors/Master’s degree in: Electrical /Computer Engineering or equivalent.
  ·Industry experience in Digital IC design with frontend/backend flow is highly desirable
  ·Strong analytical skills and good knowledge in either one of the below
        ·Digital circuit design with Synopsys/Cadence tools
        ·Machine Learning in Matlab, FPGA design using HDLs  
  ·Good interpersonal and English language skills (IELTS/ToEFL or willing to take)
  ·Be enthusiastic and willing to learn new concepts and ideas.

可以2025年秋季入学吗，25年1月份才可以拿到硕士学位证书，会对申请有影响吗？

不好意思歪个楼，但我有点好奇2000€ per month在都柏林真的能cover living cost嘛。QAQ