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activities, i.e., teaching and supervision of BSc and MSc student projects at DTU. We are looking for candidates with Strong skills in AI, Machine Learning, and/or Data Science, preferably with experience in
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, Industrial Engineering, or related discipline; Affinity and/or experience with computer programming, statistical learning, and optimization techniques; A good team spirit and feel at home at the intersection
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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Job Description Are you interested in developing novel machine learning methodologies that are scalable, reliable and explainable and that can address imminent challenges? Responsibilities and
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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publications. Desired: Experience in nanopore and/or other single-molecule experiments and their interpretation Coding skills for advanced data analysis, machine learning, kinetic modeling, etc. Nanofabrication
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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key
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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters
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at the Department of Computer and Information Science will focus on the usability and interaction with semantic technologies. Based on the semantic web technology stack for implementing DPPs, and an ontology-based