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/VHDL and C/C++ • Understanding of machine learning frameworks (e.g., Scikit-learn, TensorFlow Lite) • Demonstrated interest or experience in energy systems, NILM, or edge AI • Experience in
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development experience in the following areas: Machine Learning/AI, Internet of Things technologies. For further information, please contact Prof Gyu Myoung Lee G.M.Lee@ljmu.ac.uk . In return, we offer
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and Computer Science AI/Machine Learning Nanotechnology/Devices (more...) Artificial Intelligence, Machine Learning and Autonomy Health Tech Microelectronics BioNanotechnology Material Science and
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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into your PhD dissertation, supported by experienced GEM researchers; you design and apply innovative computational methods such as machine learning, to extract meaningful insights from GEM and complementary
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of robotics, electromobility and autonomous driving. We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and
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below, this should include: Your CV A motivation letter An academic transcript Your contact details An academic referee For more information, contact Prof. Michel van Putten (CNPH) or Dr. Maryam Amir
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variants of the sodium channel Nav1.1, which are associated with different forms of epileptic syndromes and migraine. The aim of the project is to use machine-learning assisted molecular dynamics simulations
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critical component analysis, and (iii) development of Automation of ML model and data selection. The applicants should have knowledge of machine learning and optical networks and willing to engage in testbed