"Patients suffering from sensori-neural hearing loss caused by damaged hair cells in the cochlea and diagnosed as being profoundly deaf, are potential candidates for cochlear implantation. Today, there are a number of important limitations with respect to the optimal use of these devices in deaf patients. Firstly, cochlear implant speech processors need to be adjusted so that sounds perceived by the patient are representational and at a comfortable level. Manual fitting, currently the norm, is technically demanding and time consuming and clearly suboptimal, as it involves only two of the many electrical parameters in the speech processor. Secondly, manipulation of the implant settings is based on subjective judgments of the patient , which are often inconsistent and do not reflect the outcomes on psychoacoustic measures. For the last few years, experts in the field have expressed the need for a new fitting process that optimizes the patient’s hearing in a more efficient and accurate way. For this to happen, the fitting procedure should change from a comfort-driven approach to an outcome-driven one. It should also address as many electrical parameters as possible. Ideally, a cochlear implant should come with an assisted or (semi-)automated fitting procedure in which a large number of parameters may be adjusted, based on measured psycho-acoustic feedback from the implant user. Such an assisted fitting process would drastically reduce the number of man-hours of fitting during the lifetime of the device with qualitative with qualitatively better outcomes. The main objectives of the proposed research project are therefore (i) to turn an existing theoretical automated fitting model into a clinical application by means of various techniques from statistics, machine learning and optimisation; (ii) to develop an evaluation tool to measure functional hearing capacities, in casu the ability to understand speech-in-noise, representative for day-to-day listening situations."