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PhD position on Fair and Inclusive Self-Supervised Learning for Speech Technologies (Paris/Grenoble)
The ANR project E-SSL (Efficient Self-Supervised Learning for
Inclusive and Innovative Speech Technologies) will start on November
1st. Self-supervised learning (SSL) has recently emerged as one of the
most promising artificial intelligence (AI) methods as it becomes now
feasible to take advantage of the colossal amounts of existing
unlabeled data to significantly improve the results of
various systems.

Speech technologies are widely used in our daily life and are
expanding the scope of our action, with decision-making systems,
including in critical areas such as health or legal aspects. In these
societal applications, the question of the use of these tools raises
the issue of the possible discrimination of people according to
criteria for which society requires equal treatment, such as gender,
origin, religion or disability... Recently, the machine learning
community has been confronted with the need to work on the possible
biases of algorithms, and many works have shown that the search for
the best performance is not the only goal to pursue [1]. For instance,
recent evaluations of ASR systems have shown that performances can
vary according to the gender but these variations depend both on data
used for learning and on models [2]. Therefore such systems are
increasingly scrutinized for being biased while trustworthy speech
technologies definitely represents a crucial expectation.

Both the question of bias and the concept of fairness have now become
important aspects of AI, and we now have to find the right threshold
between accuracy and the measure of fairness. Unfortunately, these
notions of fairness and bias are challenging to define and
theirmeanings can greatly differ [3].

The goals of this PhD position are threefold:
- First make a survey on the many definitions of robustness, fairness
and bias with the aim of coming up with definitions and metrics fit
for speech SSL models
- Then gather speech datasets with high amount of well-described
metadata
- Setup an evaluation protocol for SSL models and analyzing the
results. The PhD position will be co-supervised by Alexandre Allauzen
(Dauphine Université PSL, Paris) and Solange Rossato and François
Portet (Université Grenoble Alpes). Joint meetings are planned on a
regular basis and the student is expected to spend time in both
places. Moreover, two other PhD positions are open in this
project. The students, along with the partners will closely
collaborate. For instance, specific SSL models along with evaluation
criteria will be developed by the other PhD students.


Skills
- Master 2 in Natural Language Processing, Speech Processing, computer
science or data science.
- Good mastering of Python programming and deep learning framework.
- Previous experience in Self-Supervised Learning, acoustic modeling
or ASR would be a plus
- Very good communication skills in English
- Good command of French would be a plus but is not mandatory

To apply, send a CV and a cover letter to A. Allauzen before September
the 12th


[1] Mengesha, Z., Heldreth, C., Lahav, M., Sublewski, J. & Tuennerman,
E. “I don’t Think These Devices are Very Culturally Sensitive.”—Impact
of Automated Speech Recognition Errors on African Americans. Frontiers
in Artificial Intelligence 4. issn:
2624-8212. https://www.frontiersin.org/article/10.3389/frai.2021.725911(2021).
[2] Garnerin, M., Rossato, S. & Besacier, L. Investigating the Impact
of Gender Representation in ASR Training Data: a Case Study on
Librispeech in Proceedings of the 3rd Workshop on Gender Bias in
Natural Language Processing (2021), 86–92.[3] Mehrabi, N., Morstatter,
F., Saxena, N., Lerman, K. & Galstyan, A. A Survey on Bias and
Fairness in Machine Learning. ACMComput. Surv. 54. issn:
0360-0300. https://doi.org/10.1145/3457607 (July 2021).


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Alexandre Allauzen
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PhD in International and Public Law, Ethics and Economics for Sustainable Development (LEES)

Università degli Studi di Milano



The PhD Programme in Law, Ethics & Economics for Sustainability (LEES) is an interdisciplinary Program of the University of Milan, characterised by a large network of international cooperation worldwide (see the list and the international scientific committee). The LEES aims at the creation of a global interdisciplinary research community that shares a commitment to the goals of sustainability.

Such a community will be devoted to promoting an interdisciplinary, integrated research approach to global concerns, able to foster a process of change in which the exploitation of resources, the direction of investments, the orientation of technological innovation, the model of economic development and organization, and the resulting institutional change, are all made consistent with the future, as well as the present needs of humankind, granting self-determination, equal treatment and social justice to each of its members, in harmony with the preservation of the ecosystem.



VACANCIES

3 Doctoral Research Positions in Law, Ethics and Economics for Sustainable Development (LEES)

The LEES program is seeking three outstanding and committed students to carry out a three-year multidisciplinary research project, based at more than one participating university (see the call for application and the course website).

We are launching LEES, a doctoral programme in International and Public Law, Ethics and Economics for Sustainable Development. With courses, seminars, and scientific research activities entirely in English, it addresses the complexities involved in sustainable development and uses an innovative multidisciplinary approach that combines the contributions of law, ethics, and economics.





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Staff - Doctoral Programme
International and Public Law, Ethics and Economics for Sustainable Development - LEES
Website - lees@unimi.it
University of Milan
Via Festa del Perdono n° 7 - 20122 Milano, Italy

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La Statale per il futuro
Salute, transizione digitale, sostenibilità
Il tuo 5xmille ai nuovi progetti di ricerca dell’Università degli Studi di Milano
Codice fiscale: 80012650158
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A full Ph.D. scholarship at UNSW Sydney is available for a student to work on topics such as social and cognitive robotics. Particular areas of interest include reinforcement learning, interactive machine learning, explainability, and multi-agent systems.

UNSW is a research university placed in Sydney, one of the most liveable cities in the world. The School of Computer Science and Engineering this year celebrates 30 years of world-class education, industry-leading research, and global impact.


*** Requirements:
The minimum requirements to apply are:
- A Bachelor's degree with upper second class Honours; or
- A Master's degree with a substantial research component; or
- An equivalent qualification from a tertiary institution as determined by the Faculty Higher Degree Committee (HDC)

Additionally, it is desirable the applicant has relevant background and research experience.


*** What we offer:
The candidate will be supervised by Dr. Francisco Cruz. We offer a full scholarship for 3.5 years including AUD $28,854 per annum (2022 rate), tuition fees, and overseas student health cover (OSHC).


*** Application:
If you are interested and have related background please send the following documents to f.cruz (AT) unsw.edu.au by August 31st:

- Your CV
- Academic trannoscripts
- 2 paragraphs describing your research interests and background
- Most relevant publication, if any, or Master thesis (or equivalent)


Should you have questions or would like to discuss further details, please get in touch.

Dr. Francisco Cruz
School of Computer Science and Engineering
Faculty of Engineering
UNSW Sydney
The Media Integration and Communication Center at the University of Florence, Italy, has a fully-funded PhD position available in the National PhD Program in Artificial Intelligence. The topic of the PhD Fellowship is “Deep Reinforcement Learning for Embodied AI in Complex 3D Navigation Environments". In this three-year PhD research project we will look at fundamental issues related to embodied AI systems in open-world navigation scenarios. Techniques that will be applied include, but are not limited to, Inverse and Offline Reinforcement Learning, Online and Offline Continual Learning, Few-shot Imitation Learning, and Open Set Visual Recognition. Applications to be considered are diverse and include open-world navigation, natural language goal specification, visual anomaly and novelty detection, video game AI, and automated video game playtesting and validation.

The successful candidate will join in national and international collaborations with academic and industrial partners. Excellent communication skills in English are mandatory.

The application deadline is August 8, 2022. Interested candidates should contact andrew.bagdanov@unifi.it for more information.
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