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
Students enrolled in a professional higher technical course, in a degree, in an integrated master's degree or in a master's degree.
Minimum profile required
Minimum or equal grade of 16 in the Bachelor studies.
Preference factors
Experience in Computer Vision and Machine Learning.
Application Period
Since 17 Nov 2022 to 02 Dec 2022
[Closed]
Centre
Telecommunications and Multimedia