We are looking for a Project Officer to work on a Temasek Lab funded project on innovative methods for machine learning assisted TDOA-FDOA geolocation in a noisy environment. Following are the major targets of the present project:
- Extract sufficient statistics in noisy environments
- Devise highly efficient ML-assisted compressive sensing algorithms suitable for the geolocation applications
- Estimate the co-ordinates of an emitter using an ML-assisted hybrid TDOA- and FDOA-based method that are independent of structure or preamble of the data
- Compare the performance of various ML methods with non-ML-based methods
- Validate the developed methods using real data
- Conduct in-depth research on the current literature in ML-assisted geolocation and compressive sensing
- Develop ML-based methods and algorithms to achieve at least 10x improvement in compression against the traditional methods
- Integrate the traditional TDOA/FDOA-based geolocation approaches with ML
- Master degree in Wireless communications, signal processing or related fields. Will also consider candidates with good Bachelors (Honours) degree and substantial relevant work experience.
- Good experiences in one or more of the following areas: Geolocation identification algorithms, Communication system design, Wireless Communications, Compressed sensing and machine learning algorithms
- Excellent programming skills in MATLAB, SIMULINK, C/C++, etc
- Excellent analytical, technical and problem solving skills
- Good publication record in reputable journals and/or conferences
- Innovative, resourceful, and self-motivated.
- A team player with good people skills
- Strong verbal and written communication skills
We regret that only shortlisted candidates will be notified.