Help revolutionize healthcare! Develop innovative technologies to improve soundscapes in critical care units, enhancing patient safety and nurse wellbeing through cutting-edge acoustic solutions.
Job description
Noise pollution causes harmful health effects. Paediatric Intensive Care Units (PICUs) are particularly sensitive environments, which can negatively alter the physiological and emotional development of neonatal and paediatric patients. Current solutions for managing sound in PICUs focus primarily on quantifying sound pressure levels (SPL) in decibels and occasionally displaying visual cues when thresholds are exceeded. However, these approaches fall short in addressing the complex human perception of sound and fail to assist clinical staff in interpreting or mitigating harmful noise. They offer limited insight into how specific sound events affect patient well-being or staff performance, and they fail to take into account psychoacoustic and contextual dimensions that play a critical role in shaping the overall experience of sound in sensitive care environments.
To overcome these limitations, we are developing a novel digital platform-Auditory Footprints-which aims to provide real-time, perception-informed soundscape analysis in PICUs. As part of this project, we invite applications for a PhD position focused on the development of algorithms that will form the core of this platform. This work will support the definition and implementation of a Sound Quality Index (SQI), a new metric that reflects both the physical and perceptual dimensions of indoor hospital acoustics.
The research will involve modelling multiple layers. First, traditional acoustic metrics will be extracted from experimental recordings in hospitals and analysed over time. In parallel, state-of-the-art psychoacoustic metrics-such as time-varying loudness, sharpness, roughness, fluctuation strength, and tonality-will be employed to characterise how sounds are experienced by humans (e.g. In listening experiments in our laboratories). Together, these models will enable a detailed understanding of both the physical and perceptual sound environment.
Building on these foundations, the research will then explore how sound perception is influenced by contextual and affective factors. Using statistical methods, the PhD candidate will develop predictive tools for estimating the perceived affective quality of the soundscape, framed by the Pleasantness and Eventfulness dimensions described in ISO 12913-3. These models will be trained and validated using real perceptual data from PICU nurses' ratings collected during the initial stages of the project.
A further component of the work will involve the creation of an automatic sound event classification system. Using audio data annotated by perceived acoustic similarity and conventional metrics and potentially employing deep learning techniques, such as convolutional recurrent neural networks (CRNNs), the candidate will develop algorithms capable of accurately identifying key sound events (e.g., alarms, human speech, and mechanical noise). These events, once classified, will contribute to the computation of the SQI and provide actionable feedback to clinical staff.
Finally, contextual information such as time of day, room occupancy, and nurse shift data will be used to dynamically weight the SQI, ensuring that the sound quality assessments reflect the operational realities of the clinical environment. The result will be a flexible, integrated index that synthesises acoustic, psychoacoustic, perceptual, and contextual data into a real-time metric suitable for implementation in clinical settings.
This PhD project offers a unique opportunity to contribute to a pioneering interdisciplinary initiative that merges sound computing, machine learning, human perception modelling, and healthcare research. The candidate will collaborate with experts from academia, hospitals, and industry to create a solution that not only pushes the boundaries of indoor soundscape research but also has a direct societal impact on improving healthcare environments and outcomes.
Job requirements
- MSc Degree in Computer Science, Acoustics, Audio Engineering, or related fields.
- Strong background in physics and mathematics, ideally knowledge in sound computing, signal processing, and psychoacoustic modelling.
- Strong background in scientific programming, e.g., MATLAB, Python, R.
- Experience with signal processing and machine learning for audio (e.g. Speech modelling, sound event detection, source separation, etc) is an advantage.
- Ability to learn independently and passion for research.
- Strong communication skills in English.
- Team player, open to discussion and constructive criticism.
- Open-minded and excited for multidisciplinary input.
- Positive attitude to diverse approaches and inclusive behaviour.
- Previous involvement in scientific research is a plus.
- Interest in healthcare solutions is highly valued.
Challenge. Change. Impact!
Faculty Industrial Design Engineering
Matching the evolution of people with the speed of the revolution of technology. This is the focus of the Faculty of Industrial Design Engineering (IDE). Delft designers act as a bridge between advances in technology and the needs of people, organisations and society to create products, services and systems with purpose.
IDE is a leader in design research across the application areas of mobility, sustainability and health, as well as its development of design tools and methods. A 350-strong research team and over 2,000 students work together in our inspiring hall, labs and studios.
In close cooperation with industry, the public sector and NGOs we rehearse possible futures in research and education to design for a complex future.
here to go to the website of the Faculty of Industrial Design Engineering.
Conditions of employment
Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €2901 per month in the first year to €3707 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.