Verhulst

Ghent University’s Department of Information Technology invites applications for a fully funded doctoral fellow position in the Hearing Technology Lab, focusing on personalized audio signal processing and auditory neuroscience. This opportunity is part of the European Research Council-funded InSilicoEars project, which aims to develop next-generation, bio-inspired audio technologies by integrating machine learning with biophysical auditory models and neuroscience data from both normal and impaired hearing. The successful candidate will join a vibrant research team led by Professor Verhulst, working alongside biomedical engineers and audiologists. The lab is renowned for its pioneering approaches to understanding hearing loss, building biophysically inspired auditory models, and applying innovative machine learning techniques to advance audio processing. Facilities include high-performance computing clusters, local GPUs, CPU servers, and the SOUNDlab core facility with soundproof booths and integrated EEG and auditory perception setups. Your research will focus on leveraging biophysical auditory models within deep neural network-based closed-loop systems to develop novel machine-learning approaches for personalized and augmented hearing. Tasks include refining methods using neuroscience data, conducting rigorous technical evaluations and benchmarking, optimizing computational performance for real-time processing, developing hardware demonstrators, and designing sound perception experiments with human participants. Supporting clinical studies to assess the effectiveness of these technologies is also integral to the role. Applicants must hold a master’s degree in engineering sciences or informatics (biomedical engineering, computer science, information technology, electrical engineering, acoustics). Required skills include expertise in digital signal processing (audio, biomedical), experience with machine-learning techniques for time-series and frequency-domain processing, and strong programming skills in Python and MATLAB. Experience with Keras and PyTorch is essential, while knowledge of firmware programming languages (C/HDL) and prior experience with personalized audio signal processing are advantageous. Candidates should be able to work independently, communicate research results effectively, and demonstrate proficiency in English. The position offers a full-time contract for up to 48 months, with initial appointment for 12 months and extension subject to positive evaluation. Funding is provided through the ERC-InSilicoEars project, covering 100% of the net salary of an AAP member, determined by family status and seniority, and is free of personal income tax. Ghent University staff enjoy additional benefits such as training opportunities, generous holiday leave, bicycle allowance, and eco vouchers. To apply, submit your CV, diploma copy, MSc thesis, and motivation letter including contact information of two referees to [email protected] before the deadline of June 1, 2026. For further information, contact Dr. Lien Tack at [email protected]. More details about the project and lab can be found at the provided links.

The Department of Information Technology at Ghent University is offering a fully funded doctoral fellowship in the Hearing Technology Lab, focusing on personalized audio signal processing and auditory neuroscience. This position is part of the European Research Council-funded InSilicoEars project, which aims to develop next-generation, bio-inspired audio technologies by integrating biophysical auditory models with state-of-the-art machine learning approaches. The research will leverage deep neural networks and auditory neuroscience data to create robust, personalized audio solutions for complex, noisy environments. As a doctoral fellow, you will work within the Hearing Technology Lab, led by Professor Verhulst, collaborating with a multidisciplinary team of biomedical engineers and audiologists. Your responsibilities include developing novel machine-learning methods for personalized and augmented hearing, refining these approaches using neuroscience data, and conducting rigorous technical evaluations and benchmarking. You will optimize computational performance for real-time audio processing and develop hardware demonstrators. Additionally, you will design and conduct sound perception and listening experiments with human participants, and support clinical studies to assess the effectiveness of the developed technologies. The lab is equipped with advanced computational resources, including local GPUs, CPU servers, and access to high-performance computing clusters. The SOUNDlab core facility provides soundproof booths and integrated EEG and auditory perception setups. The team has a strong track record in research valorization, translating scientific findings into clinical and market applications, with support from the university’s technology transfer office. Applicants must hold a master’s degree in engineering sciences or informatics (biomedical engineering, computer science, information technology, electrical engineering, acoustics). Required skills include expertise in digital signal processing (audio, biomedical), experience with machine-learning techniques for time-series and frequency-domain processing, and strong programming abilities in Python and MATLAB. Familiarity with machine learning frameworks (Keras, PyTorch), firmware programming languages (C/HDL), and personalized audio signal processing is advantageous. Candidates should be able to work independently, communicate research results effectively, and demonstrate proficiency in English. The position offers full funding for up to 48 months, with an initial 12-month contract extended upon positive evaluation. The fellowship amount is 100% of the net salary of an AAP member, determined by family status and seniority, and is free of personal income tax. Ghent University provides additional staff benefits, including training opportunities, 36 days of holiday leave, bicycle allowance, and eco vouchers. To apply, submit your CV, diploma copy, MSc thesis, and motivation letter (including contact information for two referees) to [email protected] before the deadline of June 1, 2026. For further information, contact Dr. Lien Tack at [email protected]. More details about the project and lab can be found at the provided links.