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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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need expert knowledge in bioinformatic data analysis. Strong expertise in multi-omics data analysis (using R and Python) and a deep understanding of machine-learning models are must-criteria
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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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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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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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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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similar field; expertise in programming skills and statistical data analyses, including machine learning; affinity with environmental exposure modelling and high-performance computing; strong reporting and
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interest in environmental health and Exposome research; expertise in programming and quantitative data analysis, including machine learning in R/Python; affinity with bioinformatics; strong collaboration
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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
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., health and climate/environmental data) and could include a range of data science methods, such as utilising geographical information systems (GIS), statistical analysis, machine learning, deep learning