- From: Amal Zouaq <amal.zouaq@gmail.com>
- Date: Fri, 8 Sep 2023 08:30:21 -0400
- To: semantic-web@w3.org
- Message-ID: <CALq95+1A8ZWCnJRNdutPkMe8EQwp=Ua8niSbD+B+FHLFfDej8Q@mail.gmail.com>
Apologies for cross-postings. The LAMA-WeST laboratory (http://www.labowest.ca/) 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. *Requirements* 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 required. Please send to amal.zouaq@polymtl.ca 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 http://www.polymtl.ca/expertises/zouaq-amal http://www.labowest.ca/
Received on Friday, 8 September 2023 12:30:39 UTC