Research Fellow (Signal Processing and Machine Learning)
NTU Main Campus, Singapore
Nanyang Technological University
Nanyang Technological University is one of the top universities in Singapore offering undergraduate and postgraduate education in engineering, business, science, humanities, arts, social sciences, education and medicine.School of Electrical and Electronic Engineering is one of the founding Schools of the Nanyang Technological University. Built on a culture of excellence, the School is renowned for its high academic standards and research. With over 3,000 undergraduates students and 1,000 graduate students it is one of the largest EEE schools in the world and ranks 10th in the field of Electrical & Electronic Engineering in the 2024 QS World University Rankings by Subjects.
Today, the School has become one of the world’s largest engineering schools that nurtures competent engineers and researchers. Each year, the School graduates over a thousand students who are ready to take on great ambitions and challenges.
For more details, please view: https://www.ntu.edu.sg/eee
We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods for communication networks.
Key Responsibilities:
Develop signal processing and machine learning algorithms and methods for communication networks.
Perform software/hardware implementation and empirical studies.
Prepare report and conduct presentations at seminars.
Assist PI in proposal writing and help to supervise of graduate students.
Job Requirements:
Ph.D. in Electrical Engineering, Computer Science, Statistics, or other related fields.
Solid Mathematical skills.
Experience in implementing algorithms for signal processing and machine learning.
Good written and oral communication skills.
We regret to inform that only shortlisted candidates will be notified.
Hiring Institution: NTU* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Computer Science Engineering Machine Learning Research Statistics
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