33 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" PhD positions in Sweden
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. Your profile You have documented expertise in marine ecology and computer vision and machine learning methods for video-based fish monitoring. You have excellent IT skills and experience in handling
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world. We look forward to receiving your application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is
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of extensive datasets. You will be supervised researchers who collectively offer expertise in computational biology, genetics, epidemiology, and machine learning. The research will be closely linked
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LTU’s strong established competence networks. Duties As a PhD student, you will perform both experimental and theoretical work. You will learn how to collect and analyze scientific data within your
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communicated to practitioners through LTU’s established competence networks. Duties As a PhD student, you will perform both experimental and theoretical work. You will learn how to collect and analyze scientific
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, undergraduate and postgraduate education in communications engineering, statistical signal processing, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en
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: Analyze spectroscopic and kinetic data, employ statistical and machine learning approaches where relevant, and contribute to manuscripts, presentations, and reports. Collaboration: Work closely with project
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/isy/elda . To learn more about the Department of Electrical Engineering, see: https://liu.se/en/article/open-positions-at-isy . The employment When taking up the post, you will be admitted
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(especially neuromorphic) device, circuit and system design, clean-room fabrication, ferroelectric (or antiferroelectric) materials and devices, memristors, tactile sensors, FPGA/MCU/API development, machine
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, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree