Postdoctoral Fellow in Biomedical Data, Trinity College Dublin, Ireland

Postdoctoral Fellow in Biomedical Data: We are looking for a highly motivated postdoctoral researcher to join our research project focusing on “Temporal knowledge graph embeddings for biomedical data”. This project is a collaboration between Trinity College Dublin and Accenture Labs in Dublin. In this role, the successful candidate will work on cutting-edge research into predicting patient outcomes and trajectories from structured medical records using innovative temporal graph embedding techniques.

Postdoctoral researcher in the field of temporal knowledge graph embeddings for biomedical data

Designation: Postdoctoral Fellow

Research area: Temporal knowledge graph embeddings for biomedical data

Location: Trinity College Dublin, with joint project work at Accenture Labs in Dublin

Suitability/Qualifications:

• Ph.D. in Computer science, biomedical informatics, data science, artificial intelligence or a closely related field
• Proven research experience in knowledge graphs, temporal data analysis, machine learning or related areas
• Knowledge of programming languages ​​such as Python, R or Julia and experience with deep learning frameworks
• Solid knowledge in the construction, representation and argumentation of knowledge graphs as well as experience with temporal data analysis, machine learning models and graph neural networks
• Understanding of biomedical data, including their structure, types and sources, and familiarity with biomedical ontologies and standards
• Excellent communication skills, ability to work effectively in a multidisciplinary team environment and good project management experience

Job description:
The successful candidate will work on a research project focused on temporal knowledge graph embeddings for biomedical data and will investigate temporal graph embeddings to predict patient outcomes from medical records. This work will involve analyzing patient data as a sequence of events and developing state-of-the-art methods for temporal knowledge graph embeddings. The candidate will evaluate existing time-aware graph embedding methods and contribute to the development of new techniques. Close collaboration with a multidisciplinary team and effective communication of complex technical concepts are essential aspects of this role.

How to apply:
Interested candidates should submit a CV and a cover letter (1 A4 page) clearly outlining their experience and acquisition of knowledge by the closing date. Applications can be submitted via the following link: Apply now

Last application date: 9 September 2024

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