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The research organization is made up of 400+ researchers representing virtually every major scientific and engineering discipline. Collectively, they are driving major research programs and initiatives to decarbonize power, accelerate renewables, and promote electrification and the creation of a 21st century grid fit to power a zero-carbon energy future.We are seeking a highly motivated and talented individual to join our team as an Advanced Research Intern in the field of Aritificial Intelligence. This internship offers an exciting opportunity to work on cutting-edge research projects, collaborate with experienced researchers, and contribute to the advancement of knowledge in Machine learning theory and applications.\nJob Description\nRoles and Responsibilities\nAssist in designing and developing research experiments to address key challenges in time series analysis, machine vision, and deep learning applications.\nDevelop novel AI/ML algorithms and implement them in Python, PyTorch, TensorFlow, or similar environments.\nBuild and evaluate machine learning and deep learning models for the analysis of time series data and visual data; conduct experiments to validate algorithm performance across various datasets and scenarios.\nAnalyze experimental results to validate theoretical assumptions, assess model accuracy, and derive actionable insights for further refinement.\nConduct comprehensive literature reviews to stay abreast of state-of-the-art techniques in time series forecasting, computer vision, and deep learning architectures.\nDocument research methodologies, experimental setup, results, and findings in technical reports, papers, and presentations for both internal and external dissemination.\nRequired Qualifications\nCurrently enrolled in a Master’s or Ph.D. program in Computer Science, Electrical/Electronics Engineering, Applied Mathematics, Data Science, or a related field, with a focus on AI, Machine Learning, or Data Analytics.\nExperience developing in Python and familiarity with AI/ML frameworks such as PyTorch, TensorFlow, or Keras.\nProficiency in building and evaluating statistical, machine learning, and deep learning models for time series and/or image data.\nExperience with data processing, feature engineering, and data visualization tools and libraries (e.g., NumPy, pandas, matplotlib, OpenCV).\nExcellent analytical and problem-solving skills, with the ability to critically evaluate research findings and propose innovative 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