Some recent advances in machine learning have led to feature learning
methods that can learn the regularities (i.e., the
“conceptualization”) underlying a domain. Ontologies are the explicit,
symbolic representations of these conceptualizations. There still
remains a gap in AI between the distributed representations that are
learned by recent machine learning algorithms and the symbolic
representations of the same phenomena. Closing this gap is a major
remaining challenge for AI with significant impact in biology and
biomedicine in which a large number of symbolic representations
(ontologies and knowledge graphs) have been developed and are
applied. In this project, the PhD student will work on the boundaries
between statistical and symbolic approaches to AI and develop novel AI
methods with applications in biology and biomedicine. Major
application areas include understanding molecular mechanisms
underlying traits, phenotypes, and disease, and identifying ways to
perturb biological systems through bioactive compounds (drugs).
The student will join a productive research team at one of the
fastest-growing research universities, located in Thuwal, KSA, by the
King Abdullah University of Science and Technology
– MSc degree (of BSc degree for MS+PhD applicants) at the
Commendation/Distinction level in computer science, electrical
engineering, or mathematics.
– Excellent programming skills.
– Experience in machine learning, optimization, knowledge
representation, Semantic Web technologies, or bioinformatics is
desirable but not required.