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using advanced computational, statistical, machine learning, and, ideally, mixed-methods approaches that leverage both qualitative and quantitative data related to real-world behavioral, physical activity
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and data analysis), synchrotron-based techniques, machine-learning and non-learning based approaches to data analysis, and familiarity with unconventional superconductivity. Responsibilities: • Perform
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allow for more distributed and variable generation. Job requirements Essentials: Basic knowledge of power systems and machine learning/AI. Completed an MSc degree in a highly technical related discipline
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lie at the crossroads of multiple disciplines and involve expertise in optics, electronics, image and data processing (including machine learning), photophysics, chemistry and biology. The position is
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experience in modern statistical methods, statistical and machine learning, and AI is advantageous Excellent knowledge of SPSS; knowledge of STATA and/or R and/or Python is an advantage. Interest in mixed
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graduate credit hours in the teaching discipline is required at the time of hire. To teach graduate courses: A Terminal/Doctorate degree, or the foreign equivalent, in Electrical and/or Computer Engineering
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-rank faculty positions within its Computer Science Program. The search will prioritize candidates who specialize in Artificial Intelligence (AI) and Machine Learning (ML), including but not limited
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://sandrasiby.github.io/ . The lab website can be found at https://sites.google.com/nyu.edu/haven-lab The successful candidate will engage in research that includes applied machine learning, systems building, and Internet
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Experience and skills · 1-2 years of post-PhD research and engineering experience · Excellent knowledge in information security, particularly in machine learning for cybersecurity
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Institute of Organic Chemistry and Biochemistry of the CAS (IOCB Prague) | Italy | about 2 months ago
The Pluskal lab at IOCB Prague combines cutting-edge experimental (e.g., LC-MS, metabolomics, RNA-seq) and computational (e.g., bioinformatics, molecular networking, machine learning) approaches