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frameworks. This approach is timely, as improvements in machine learning (ML) applications now allow researchers without extensive programming backgrounds to implement advanced image-processing techniques
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engineering innovation by developing wildlife tracking technologies, including integrating advanced AI-based analytics to create a novel prototype for conflict mitigation. The work will involve developing a
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resonant acoustic mixing (RAM) – a relatively novel processing methodology which is of increasing industrial interest spanning multiple sectors. The project will allow the candidate to explore a number of
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Supervisor: Jaan Praks Offered by: Aalto University, School of Electrical Engineering General description of programme and host The Electric solar wind Sail doctors (E-Sailors) is a challenge-based Doctoral
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quality, diversity, and biological relevance using standard metrics and expert review. Anonymised digital images from tissues in biobanks will be used to train generative models on university computing
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speed - Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace
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marine sciences, biological oceanography, ecology, or computer sciences. Strong analytical, numerical and practical skills are essential. Experience in coding or applying quantitative methods in a
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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
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: Applicants should have, or expect to achieve, at least a 2:1 honours degree (or equivalent) in a relevant subject, typically in the physical sciences or engineering. Experience of computer simulation, or
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, using signal processing/machine learning techniques, to realise all-weather perception in autonomous vehicles with high-quality multiple-input-multiple-output (MIMO) radar sensing/imaging. The project