Post-Doc Position in AI-based Face Recognition Explainability

Face recognition has become a key technology in our society, frequently used in multiple applications, while creating an impact in terms of privacy. As face recognition solutions based on artificial intelligence (AI) are becoming popular, it is critical to fully understand and explain how these technologies work in order to make them more effective and accepted by society. In this project, we focus on the analysis of the influencing factors relevant for the final decision of an AI-based face recognition system as an essential step to understand and improve the underlying processes involved. The scientific approach pursued in the project is designed in such a way that it will be applicable to other use cases such as object detection and pattern recognition tasks in a wider set of applications. Thanks to the interdisciplinary nature of the consortium, the outcomes of XAIface will affect many fields and can be summarized as follows: (i) develop clear legal guidelines on the use and design of AI-based face recognition following the privacy-by-design approach; (ii) disentangling demographic information (age, gender, ethnicity) from the overall face representation in order to understand the impact of such traits on face recognition but also to develop demographic-free face recognition; (iii) address fairness and non-discrimination issues by following the idea of de-biasing during the training; (iv) optimize the tradeoff between interpretability and performance; (v) create tools that will allow assessment and measurement of performance and explanation of decisions of AI-based face recognition systems; (vi) analyze image coding impact to better understand how future AI-based coding solutions may be different from a recognition explainability point of view.

This project includes several international teams and will last for 3 years. The working place will be at Instituto de Telecomunicações, Instituto Superior Técnico, Lisboa, Portugal.

Research grant: The research grant is associated to a yearly renewable contract (up to 3 years) that includes an experimental period of 6 months. The research grant consists on a tax-free stipend of 1616€ per month. The candidates must fulfill the following conditions:

• PhD in computer science, electrical and computer engineering or other relevant area, awarded in the past three years.
• Preference will be given to candidates knowledgeable in machine learning, computer vision, multimedia signal processing and face recognition.
• Strong motivation to perform research, to participate in a rich and stimulating international project, and to advance state-of-the-art through the publication of results in peer reviewed international conferences and journals.
• Fluent in English and with good skills in technical writing and presenting.
• Good programming skills (Python, C/C++) are required.

The selected candidate will work in a team lead by Prof. Fernando Pereira and Prof. João Ascenso (see http://www.img.lx.it.pt/Staff.html for details). The candidates will join a team of staff and PhD students where intense research and development activities in the multimedia signal processing and machine learning fields are carried out. 

To apply, please submit your application by sending an email to Prof. Fernando Pereira and Prof. João Ascenso at fp@lx.it.pt and joao.ascenso@lx.it.pt with the following documents:

1. Detailed curriculum vitae with transcripts
2. Motivation letter (research statement) explaining your interest in the position
3. Recommendation letter(s)

Applications shall be received until suitable candidates are found but before 15/2/2021. Selected candidates will be interviewed. For any clarifications, please contact Prof. Fernando Pereira and Prof. João Ascenso.