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Computer Vision There is growing trend towards explainable AI (XAI) today. Opaque-box models with deep learning (DL) offer high accuracy but are not explainable due to which there can be problems in
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(EoS), or machine learning approaches. Hands-on experience in extracting bioactive compounds from biomass. Strong collaboration skills and the ability to work effectively in interdisciplinary teams. A
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within the broad topics of modelling tool-workpiece interaction in mechanical material removal processes, zero-defect manufacturing, machining system performance characterization as well as on-machine and
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Job Description Are you passionate about brain-computer interfaces (BCIs), neurorehabilitation, and intelligent assistive technologies? The Department of Health Technology (DTU Health Tech
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undermine this future. Can you see how Machine Learning, Computer Vision, and Robotics can open up opportunities for autonomously operating agricultural robots? Are you passionate about making agriculture
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are looking for candidates who have experience with developing AI or machine learning models, as well as bacterial sequence analysis. You should be familiar with relevant programming languages such as Python
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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institutes, and industrial partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1