Open PostDoc position in AI and Digital Health with Knowledge Graphs

Apologies for cross-postings.

The LAMA-WeST laboratory ( is looking to recruit a
postdoctoral student for a new project at the intersection of AI and
digital health: GAI-ORKG : Generative AI for Oncology Research with
Knowledge Graphs.


The post-doctoral candidate must hold a doctorate in natural language
processing / machine learning/ Semantic Web (creation of ontologies,
knowledge bases, etc.). He or she should have an in-depth knowledge of
Python programming. Experience in the field of AI and health is a plus.

The candidate must be passionate about AI research and will contribute to
the methodology of the project, the implementation of certain models, the
supervision of doctoral and master's students, and the writing of articles.
Leadership, as well as oral and written communication skills are also

Please send to  a CV, a transcript, as well as a
letter motivating how your past experience can contribute to this project.
Please indicate in the subject of the message: Postdoc - GAI-ORKG:
Generative AI for oncology research with Knowledge Graph.

*Detailed description*

Over the last two decades, healthcare has moved from a paper-based reality
to a digital one and a trove of digital health data now exists.
Simultaneously, an era of AI has dawned with benefits to many areas of
society. However, the unstructured and siloed nature of a lot of health
data mean these parallel developments have barely converged and the
benefits of AI in healthcare remain, as yet, unrealized. This is
particularly true in cancer care. For many cancer patients, important
information is buried in clinical notes in disparate parts of their
electronic health record. Likewise, useful information, that could
otherwise contribute to AI-powered cancer research, lies trapped and
inaccessible to researchers. A solution to combine, consolidate, and
exploit unstructured health data is needed.

To achieve this objective, the research team will leverage modern standards
for health data to build/learn a cancer patient knowledge base (i.e. a
fully-structured record for each patient) from both structured and
unstructured data in electronic health records. We will investigate how
neural architectures, pretrained language models, and knowledge graphs can
be used to extract such a knowledge base and provide relevant information
to specialists through natural language generation approaches.

Dr. Amal Zouaq, Ing., PhD

FRQS (Dual) Chair in AI and Digital Health | Titulaire de la chaire
(double) FRQS en IA et santé numérique

Associate Professor | Professeure agrégée
Polytechnique Montréal
Office / Bureau: M-3416
Phone / Tel:  (514)340-4711 ext.2228

Received on Friday, 8 September 2023 12:30:39 UTC