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collected by moored probes, underwater gliders and micro-AUVs and shipborne sampling. Furthermore, parameters describing atmospheric variability and riverine inputs will be included in the analysis to better
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critical maritime operation or system Collecting and curating operational and security-related data for AI-based threat analysis Training AI and machine learning models for anomaly and threat detection
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multidisciplinary approaches, including advanced bio-imaging, image analysis, cell biology, genetics, and molecular biology. Find out more about our research group here: https://tymri.ut.ee/en/content/developmental
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Supervisor: Tenured Associate Professor Serkan Turkmen, Estonian Maritime Academy: Estonian Maritime Academy: Green Maritime Technology Research Group Proposed Thesis: Towards net zero in Maritime Industry
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latency and network dependency. On the communication side, the system will utilize standardized industrial IoT protocols such as MQTT, OPC-UA, and Modbus TCP to enable efficient, secure, and low-latency
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analysis is often conducted without a proper assessment of its effects on thermal comfort, or appropriate OTC metrics are applied only to evaluate thermal sensation under near-stationary conditions
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with offshore extreme sea levels. The project aims at applying physics-based modelling and advanced analysis to mitigate an environmental challenge with direct implications for coastal risk management
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. The successful candidate will have opportunities to collaborate with a broad global network of leading universities and research centers already engaged with the supervisory team. These collaborations include
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experimental data from IoT sensor networks and near real-time simulations. Conduct sensitivity analysis under varying operational conditions, including environmental factors such as temperature and load
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changes to hydrometeorological forcing. Geomorphology, 414, 108383, https://doi.org/10.1016/j.geomorph.2022.108383 Eelsalu, M., Viigand, K., Soomere, T., Parnell, K., 2024a. Systematic analysis