Electrical Engineering
[Closed]
Work description
- Push forward the state of the art knowledge in machine learning regarding methods for neural networks complexity reduction; - development of methods for the evaluation of biases, fairness, overestimation and related metrics; - study, development and comparison of diverse approaches to reduce the complexity of neural networks; - development of benchmarking approaches that go beyond the traditional Accuracy vs Complexity trade-off; - exercise critical thinking in evaluating the research process and the results obtained.
Academic Qualifications
The awarding of the grant is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions
Minimum profile required
Experience in Computer Vision and Machine Learning.
Preference factors
Experience in research projects, and writing of scientific papers.
Application Period
Since 03 May 2024 to 16 May 2024
[Closed]
Centre
Telecommunications and Multimedia
Scientific Advisor
Ana Filipa Sequeira