PhD Scholarship in IoT Devices: DTU Electro invites applications for a PhD position focusing on implementing and optimizing Machine Learning (ML) and Deep Learning (DL) in Internet of Things (IoT) edge devices. The successful candidate will work on practical solutions addressing real-world challenges in IoT Edge computing, collaborating with experienced researchers in a diverse and innovative environment.
PhD Scholarship in Machine Learning for IoT Edge Devices – DTU Electro
Summary Table:
- Designation: PhD Student
- Research Area: Machine Learning in IoT Edge Devices
- Location: DTU Electro, Kgs. Lyngby, Denmark
- Eligibility/Qualification: Two-year master’s degree (120 ECTS points) or equivalent academic level.
- Job Description: Drive advancements in Machine Learning, Deep Learning, and efficient implementations in IoT devices and networks. Analyze, implement, and optimize ML & DL principles on microcontrollers. Investigate use-case dependencies and explore communication channel optimization.
- How to Apply: Submit a complete online application by 20 June 2024 (23:59 Danish time), including a cover letter, CV, transcripts, and diploma in English as one PDF file.
- Last Date for Apply: 20 June 2024
Designation: PhD Student
Research Area: Machine Learning in IoT Edge Devices
Location: DTU Electro, Kgs. Lyngby, Denmark
Eligibility/Qualification:
- Two-year master’s degree (120 ECTS points) or equivalent academic level.
Job Description:
The successful candidate will be responsible for driving advancements in Machine Learning, Deep Learning, and efficient implementations in IoT devices and networks. Key responsibilities include:
- Analyzing ML vs DL working principles.
- Implementing ML & DL principles on microcontrollers using optimized embedded programming.
- Identifying ML & DL IoT use-cases and exploring future applications.
- Investigating pre-trained DL scenarios for efficient Edge computing in MCUs.
- Exploring possibilities and limitations of on-device ML/DL training.
- Overcoming challenges of high-volume training set needs through Edge crowd/swarm-training.
- Optimizing communication channels for real-time machine learning tasks at the edge.
- Investigating distributed algorithms tailored for edge learning scenarios.
- Addressing data integrity & security aspects of ML-enabled edge computing.
- Collaborating closely with industry and research partners to achieve research goals.
- Publishing research findings in leading journals and conference proceedings.
How to Apply:
Submit a complete online application by 20 June 2024 (23:59 Danish time). Applications must include a cover letter, CV, transcripts, and diploma in English as one PDF file. Apply through the provided link on the DTU website.
Last Date for Apply:
20 June 2024
