PhD Scholarship Opportunity at Monash University Malaysia
Field of interest: Music Processing with Deep Learning
Level of study: Ph.D
Tuition fee: Waived for qualified student (amounts to RM40,280 per year)
Monthly stipend: RM2.2
Duration: 3 ~ 4 years, depending on progress
Deadline: 16 July 2017
I am looking for a highly motivated Malaysian doctoral student, who is interested in performing cutting-edge research in music processing for entertainment applications, with a particular focus on deep learning (e.g, RNN and LSTM) architectures, information fusion and domain adaptation.
Prospective applicants should have:
- Strong academic record with an excellent MEng/MSc degree or **BEng/BSc (1st class honor) in computer science, applied mathematics, or electrical engineering, preferably with expertise in one or more of the following areas: machine learning, neural networks and computer vision; (** BEng/BSc holder withupper-second with related research experience can be considered, but will first pursue the MPhil program, then apply for conversion to PhD.)
- A good mathematical background;
- A basic knowledge in music theory is essential due to the nature of the research topic;
- Good programming skills in languages such as C, C++, Python and/or MATLAB, and is eager to advance the state of the art technology with deep learning algorithmic approaches or libraries (CAFFE, TensorFlow, NumPy, SciPy);
- Willing to align the fundamental research with the project requirements in collaboration with University of Malaya, as this is a joint research collaboration between two institutions.
- Able to disseminate your results via top journal publications, conference presentations, etc.
Any prior publication in major conference or journals in computer vision/machine learning is not necessary but will be a plus.
Interested applicant should contact A/Prof. Dr. Wong Kok Sheik at firstname.lastname@example.org, with the email subject of “PhD @ Monash University Malaysia”. Only shortlisted applicants will be notified and called for interview.