*Machine learning techniques for cognitive dynamic systems: application of the Bayesian approach to radio and video domains*
The proposed PhD involves the study of machine learning techniques with a Bayesian approach: the goal of the PhD is to develop novel learning techniques and apply them to possibly two different domains. On one side, bio-inspired learning approaches can be applied to cognitive radio networks thus allowing adaptive methods for analysis
and decision to evolve under specific environmental operating conditions. Focus will be on Bayesian methods and game theory approach for modelling dynamic strategies for opportunistic spectrum access.
Moreover, a Bayesian approach can be applied for learning from video data: signals acquired from both static surveillance cameras and egocentric vision sensors can be analysed for characterizing behaviours of both single users and complex active entities (e.g. crowds).
Activities will be oriented to address the use of developed methods within bio-inspired cognitive artificial systems for physical security and telecommunications.
Possibility will be open during the PhD to join on going funded research projects when available within the joint labs ISIP40 has with industries (e.g Technoaware, Telecom, ligurian industrial district SIIT).
Company:
University of Genoa
Qualifications:
1. *Laurea Vecchio Ordinamento*or*Laurea Specialistica*degree for Italian applicants in one of the following fields (or another relate field);*Master of Science*or equivalent degree for
applicants from abroad:
1. Telecommunication Engineering
2. Electronic Engineering
3. Information Engineering
4. IT
5. Computer Science (or Laurea degree in Informatica)
2. Basic knowledge in the following topics:
1. Signal Processing
2. Communication Systems
3. Pattern Recognition
4. Image/Video Processing
3. English knowledge
Language requirements:
English
Specific requirements:
1 position (3 years) starting at the beginning of October 2015; expiration date for first level applications: May 10th, 2015*
Tagged as: Academia, Bayesian Methods, Italy, Machine Learning, Pattern Recognition
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