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Field
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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pool, sauna, climbing wall, cycle circuit, and sports halls. Personal Development: Access to courses run by our Organisational Development & Professional Learning team. Access to on-site childcare
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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computational and machine learning approaches to integrate Oxford Nanopore (ONT) long-read data with bulk and single-cell RNA-seq profiles. The aim is to identify host-microbiome molecular signatures that drive
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motivated candidate with an interest in building quantum technology and learning a wide range of skills in optical systems, precision measurements, electronic control and feedback systems, vacuum systems
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for competence development and learning. We constantly keep learning to find the most impactful ways to empower – and invest in – our people. Salary will be defined according to the salary system of Finnish
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electrolyser devices. However, we do not expect you to have prior knowledge of fabricating and running electrolysers or computational models. You will get the opportunity to learn from both the experienced