Research topic description
Sign languages are the primary form of communication for many deaf or hard of hearing people.
They are also entirely separate languages, which are regional and have local variations, and generally
have very little relation to any spoken languages in the same region. For example, Flemish Sign
Language (used in Flanders) is quite different from Dutch Sign Language (used in the Netherlands),
but very similar to the French Belgian Sign Language used in Wallonia (the French speaking part of
Belgium). Without any connection between written languages and the natural language structure of
their native sign language, and without the intermediate step of the (loose) connection between
sounds and letter combinations, learning to read and write is much more challenging for people who
are born deaf, than it is for hearing people to learn a different spoken/written language.
However, only very few hearing people can use or understand a sign language, even at a basic level.
This is why deaf people often rely on human sign language interpreters to communicate with hearing
people and vice versa. Because an interpreter needs to be reserved and paid for, they are only
available at specific times, for planned interaction with hearing people (e.g., a doctor’s visit, an
administrative appointment, a job meeting, …). For unplanned or spontaneous interactions, the
resulting communication barrier often restricts interactions to the absolute minimum, e.g. by using
pointing and other gestures, facial expression or writing when possible.
One way to help mitigate the communication barrier between hearing and deaf people would be the
use of machine learning techniques. In UGent’s AIRO team, we focus on automatically extracting
information from sign language videos into a robust representation (embedding). This can then be
used in two ways: either as input for transformer-based multilingual translation models, or to
develop tools that give automatic learning support to sign language learners.
Either way, the main challenge is the fact that, compared to spoken languages, very little sign
language data is available and only a fraction of that has been labeled. This means that any technique
we develop needs to be data-efficient, and very robust to overfitting. It should also maximally use
transfer learning from related data and/or tasks, as well as unsupervised (or self-supervised) learning
to benefit from unlabeled data.
This research fits into the European project SignON (https://signon-project.eu), in which a large
consortium of partners from academia, stakeholders and industry are developing a first prototype of
an app that can translate in any direction between a number of European sign languages and a
number of European spoken languages. Each step in this project is taken in close interaction with the
user community, represented by the European Union of the Deaf as well as several regional
representatives (e.g., in Flanders, VGTC – Centrum voor Vlaamse Gebarentaal). Their input is used for
the identification of use cases and for the evaluation of our results.
Imec-IDLab-UGent
IDLab is a research group of UGent, as well as a core research group of imec. IDLab performs
fundamental and applied research on data science and internet technology, and counts over 300
researchers. Our major research areas are machine learning and data mining; semantic intelligence;
multimedia processing; distributed intelligence for IoT; cloud and big data infrastructures; wireless
and fixed networking; electromagnetics, RF and high-speed circuits and systems. The AIRO (“AI and
robotics”) team of IDLab has been studying various kinds of neural networks for more than 20 years.
Our focus is on applications in robotics and in human-AI interaction. AIRO has been at the forefront
of deep learning research ever since it became popular a decade ago. Illustrative of this success are
an excellent track record at Kaggle competitions. thanks to a consistent focus on innovative ideas
that are somewhat outside the mainstream “AI-hype”, many of our former PhD students are now
working at top commercial research labs, such as Deepmind or Google Brain, or hold strategic senior
R&D positions in companies.
Our offer:
We offer a fully funded PhD scholarship for a maximal period of 4 years. Your initial contract will be
for one year. In the case of a positive progress evaluation, you will then receive a second contract for
the remaining three years.
The PhD research has fundamental and innovative aspects, but also a clear application target. You
will research and develop data-efficient to extract the information from sign language video that is
relevant for understanding and translation the sign language and for identifying possible weaknesses
in the execution quality. You will use state-of-the art techniques from machine learning, deep
learning and language technology. You will be encouraged to publish and present your work at
project meetings and international conferences, or to attend useful summer schools.
You will join AIRO, a young and enthusiastic team of around 30 researchers, post-docs and
professors. You will also collaborate with our many Flemish and European project partners: experts
in various aspects of language technology, signing avatar generation, linguists who specialize in sign
language, and the Flemish and European deaf communities.
Interested?
Apply with the following documents (Incomplete applications will not be considered!)
- Motivation letter: explain why you are interested in and the right candidate for this
particular project! Standard motivation letters that have no relation to the research topic of
this vacancy will not be considered!
- Detailed academic results (including course list, grades and percentile)
- Pdf of Master’s thesis and relevant publications (if any), possibly a description of project
work that is relevant to the vacancy
- English proficiency scores for candidates with degrees outside the EU
- Two reference contacts
For any questions, contact prof. dr. ir. Joni Dambre (Joni.Dambre@UGent.be).
After the first screening, good candidates will be invited to perform a skills test (a deep learning
assignment).
A selection of candidates with good results on this will be invited for an interview (in-person or
online), in which they will be asked to present themselves and a scientific topic of our choice and
their skills will be assessed based on open and unprepared discussion..
Timeline and closing date
This PhD position is available as of September 1st, 2022.
You can apply until Sunday, July 20th by sending an email with all required documents attached to:
Prof. Joni Dambre – joni.dambre@ugent.be
Incomplete applications or generic applications that have no clear relation to this specific research
position will be ignored.
Very early applicants who are top candidates (that meet the requirements above) may receive a
reaction before the end of June. There will be no reactions between July 1st and July 21st.
After processing all applications, you will receive either a negative answer, or an invitation to the rest
of the procedure, which will consider of an interview, in which you will present a research-related
topic of our choice and your communication skills and technical background will be assessed during a
spontaneous discussion. We may also invite you to participate in a skills assessment in the form of a
coding assignment related to the field.
