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Biological Dark Matter (BDM). A main idea of this project is to make use of the pattern matching abilities of the Tsetlin Machine in machine learning to be able to recognize signals in the BDM in
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architectures in the field of computer vision and with training, validating and inference processes in machine learning; Familiarity with generative AI; Curious about mathematics and biology; Excellent
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on leveraging deep learning and advanced image processing techniques to improve the current tools for biomonitoring of aquatic ecosystems. This position involves the development and application of machine
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application of machine learning algorithms to automatically classify freshwater benthic diatoms at the species level and quantify key morphological traits. These advancements aim to improve ecological
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/technical challenges Project FITNESS will build upon and extend state-of-the-art methods [1], [2] recently developed within the team, showing to outperform existing, machine-learning based approaches in
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thematic areas: Control systems, computational intelligence and machine learning, autonomous systems, optimization and networks, embedded and real-time systems hardware and software, fault diagnosis, cyber
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related subject (or be close to completing the degree); demonstrated interest in AI reasoning systems, algorithms, IoT, context-aware pervasive computing, machine learning and data analysis, software
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, machine learning, remote sensing, and oceanography to tackle the challenges of capturing and interpreting complex geophysical processes. 1.5. References [1] Torres, R., Snoeij, P., Geudtner, D., Bibby, D
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optimizations and approaches inspired by machine learning within the framework of cognitive radar; and C) verify the developed approaches with suitable simulations and experimental demonstrations. Specifically
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» Programming Medical sciences » Other Researcher Profile First Stage Researcher (R1) Country Austria Application Deadline 9 Apr 2025 - 21:59 (UTC) Type of Contract Permanent Job Status Full-time Is the job