Ph.D. position in neurosymbolic learning and semantics of sounds at IDS (Institute of Data Science)

The Institute of Data Science at Maastricht University is seeking applications for a Ph.D. position to develop a neurobiologically-grounded computational model of sound recognition by combining advanced methodologies from information science, artificial intelligence, and cognitive neuroscience. This position is fully supported by a grant from the Netherlands Organisation for Scientific Research (NWO), which will recruit 2 Ph.D. students to join an existing team of interdisciplinary researchers from two groups, the Institute of Data Science and the Auditory Cognition research section at the Maastricht Brain Imaging Center. The overall aim of the research project is to unravel the neural mechanisms enabling the recognition of everyday sounds and their flexible categorization at multiple levels of semantic abstraction.

For this position, the successful candidate will develop an ontology to characterize a large number of everyday sounds and their taxonomic relation in terms of sound-generating mechanisms and properties of the corresponding sources. They will then create deep neural networks (DNNs) that combine acoustic sound analysis with ontological information over the sound sources and learn to perform sound recognition tasks at different abstraction levels. This research will create neurosymbolic representations of sounds that will be of broad academic interest and relevant for the rapidly expanding societal applications of artificial hearing. In addition, these models will provide computational hypotheses into the mechanisms of acoustic-to-semantic transformations of sounds, which will then be tested in functional neuroimaging experiments conducted by the research group.

Tasks include:

● Work with an interdisciplinary (computer science, psychology, neuroscience) team to discuss research problems

● Develop computational methods for the preparation, analysis, and dissemination of knowledge graphs and ontologies.

● Validate developed methods using synthetic and real-world data

● Communicate the results of the research to stakeholders and target groups

● Write scientific papers for international peer-reviewed conferences and journals

● Present your work at international conferences

● Provide teaching assistance in undergraduate and master’s courses

● Participate in off-site internships