National Research Council Canada (NRC) is seeking a postdoctoral candidate
with an interest in ethical aspects of Natural Language Processing (NLP).
The goal of the project is to develop tools and techniques to address two
critical issues in ethical NLP applications design and use: fairness and
explainability. AI applications often inadvertently perpetuate and
accentuate unfair biases that can originate from multiple sources, such as
training data, labeling process, data sampling, etc. Biased outputs can
negatively affect certain demographic groups of users and even lead to
discrimination. The ability of an AI system to provide understandable
explanations for its decisions is a crucial factor in real-life
applications both for developers to better understand the system?s behavior
and for users to gain trust in the system. The techniques will be developed
with the focus on one application area, abusive language detection in
social media, while ensuring their applicability and/or transferability to
other natural language understanding tasks and domains.

The successful applicant will join the Text Analytics group at NRC Digital
Technologies Research Centre, which has a world-renowned reputation in
Natural Language Processing and Computational Linguistics, including
sentiment and emotion analysis, information extraction, medical informatics
and health-related applications. The applicant will work in close
collaboration with the project leads, Dr. Svetlana Kiritchenko and Dr. Isar
Nejadgholi, but will also have access to diverse expertise of other NRC


NRC Canada


Degree: An applicant has to have obtained a Ph.D. degree within the last 5
years (or expect to obtain within the next 6 months) in one of the
following or related fields:
Required experience:

working experience in computational analysis of textual or other kinds
of data;

good working knowledge of at least one programming language, preferably

excellent written and communication skills.

An applicant has to have a working experience or ability and desire to
learn the following:

machine learning pipeline;

deep neural network algorithms;

automatic text processing;

text processing, machine learning, and deep learning libraries, such as
NLTK, spaCy, scikit-learn, keras, PyTorch.

Specific requirements:

Position duration: 2 years

Start time: flexible

Educational level:

Ph. D.

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