Postdoctoral Researcher in Biomedical Data: We are seeking a highly motivated Postdoctoral Researcher to join our research project focusing on Temporal Knowledge-Graph Embeddings for Biomedical Data. This project is a collaborative effort between Trinity College Dublin and Accenture Labs in Dublin. As part of this role, the successful candidate will work on cutting-edge research to predict patient outcomes and trajectories from structured clinical records using innovative techniques in temporal graph embeddings.
Postdoctoral Researcher in Temporal Knowledge-Graph Embeddings for Biomedical Data
Designation: Postdoctoral Researcher
Research Area: Temporal Knowledge-Graph Embeddings for Biomedical Data
Location: Trinity College Dublin, with joint project work at Accenture Labs in Dublin
Eligibility/Qualifications:
- Ph.D. in Computer Science, Biomedical Informatics, Data Science, Artificial Intelligence, or closely related field
- Demonstrated research experience in knowledge graphs, temporal data analysis, machine learning, or related areas
- Proficiency in programming languages such as Python, R, or Julia, and experience with deep learning frameworks
- Strong knowledge of knowledge graph construction, representation, and reasoning, as well as experience with temporal data analysis, machine learning models, and graph neural networks
- Understanding of biomedical data, including its 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 focusing on Temporal Knowledge-Graph Embeddings for Biomedical Data, investigating temporal graph embeddings to predict patient outcomes from clinical records. This work involves analyzing patient data as a sequence of events and developing state-of-the-art methods for temporal knowledge graph embeddings. The candidate will assess existing time-aware graph embedding methods and contribute to the development of new techniques. Strong collaboration with a multidisciplinary team and effective communication of complex technical concepts are essential aspects of this role.
How to Apply:
Interested applicants should submit a Curriculum Vitae and a Cover Letter (1 A4 page) before the closing date, clearly addressing their experience and how they obtained their knowledge. Applications can be submitted via the following link: Apply Now
Last Date for Apply: September 9, 2024
