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systems in the research project, including testing and troubleshooting. Implement and test machine learning models, which may involve data preprocessing, model training, and evaluation. Create and maintain
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Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Machine Learning for Intelligent Hearing Assistance in Complex Acoustic
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of interest include, but are not limited to, stochastic, discrete, large-scale, and data-driven optimization, machine learning methods for sequential decision making, or stochastic modeling and prescriptive
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] Subject Areas: mathematical modeling, statistics, machine learning, data-driven modeling, dynamical systems, optimization Appl Deadline: 2026/04/01 04:59 AM UnitedKingdomTime (posted 2026/02/19 05:00 AM
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, or SyCL/OpenCL. Hands-on experience with machine learning, including end-to-end training, tuning, and evaluation of at least one class of models. Working understanding of common machine learning model
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records, aiming to co-create practical tools deployable in real-world clinical settings. This work is central to a multidisciplinary collaboration bringing together experts in machine learning, neuroscience
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statistical modeling, machine learning, data analysis, and reporting Proficiency in Python or R Ability to plan, execute and control a project, establishing realistic estimates and reporting timelines Advanced
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, single-unit recordings. 2. Strong background in computation modelling of behavioral or neural data. 3. Proven experience with statistics, machine learning and/or brain stimulation. 4. Proven experience
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, test and measurement methodologies for electronic modules, system engineering, data pre-processing and database indexing/analytics for dashboarding/visualisation, embedding machine learning algorithms
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and