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to uncover SRL patterns in video-based learning (VBL) and digital game-based learning (DGBL) environments; • Conduct process mining and network analysis to differentiate SRL patterns between high- and low
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software (e.g., STATA, SPSS, NVivo) Proven track record of publications in international peer-reviewed journals Preferred Skills and Attributes Excellent written and verbal communication skills in English
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data analysis (TDA) techniques such as persistent homology, with application in real data sets such as biomolecular data and network data Perform data preprocessing, feature extraction, and analysis
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to fourth year of Project 3, the main goals are to examine the changes in students’ peer networks and quality of relationships with key significant others (i.e., peers, parents and teachers), the influence
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Science 2018). Our research combines in vivo and in vitro experimental platforms to explore mechanisms of synaptic and neural network activity dysfunction in the human neocortex. Using human iPSC-derived
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, TEM, XRD, AFM, XPS). Machine learning methods. High-throughput experiment design and data analysis. Scientific writing and communication. Data processing and visualization software (CrystalMaker
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and experience in fault diagnosis and analysis and protection systems will be advantageous • Knowledge of software such as PSIM, ETAP and MATLAB/Simulink • Prior knowledge and experience in
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should be highly proficient with a range of machine learning approaches, including unsupervised, semi-supervised, supervised, and various deep learning techniques (e.g., convolutional neural networks
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. Experience in matlab, MEEP, RCWA and other softwares and algorithms. Excellent publication track records. Good understanding of nonlinear and quantum optics. Strong knowledge of optical physics, while
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, Sustainability Science, or related field. Experience in conducting LCAs using ISO 14040/44 standards and LCA software (e.g., SimaPro, OpenLCA, GaBi). Strong knowledge of LCI databases (e.g., Ecoinvent, ELCD) and