Intern

• Assist in designing and developing research experiments to address key challenges in time series analysis, machine vision, and deep learning applications.
• Develop novel AI/ML algorithms and implement them in Python, PyTorch, TensorFlow, or similar environments.
• Build 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.
• Analyze experimental results to validate theoretical assumptions, assess model accuracy, and derive actionable insights for further refinement.
• Conduct comprehensive literature reviews to stay abreast of state-of-the-art techniques in time series forecasting, computer vision, and deep learning architectures.
• Document research methodologies, experimental setup, results, and findings in technical reports, papers, and presentations for both internal and external dissemination.

Required Qualifications

• Currently 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.
• Experience developing in Python and familiarity with AI/ML frameworks such as PyTorch, TensorFlow, or Keras.
• Proficiency in building and evaluating statistical, machine learning, and deep learning models for time series and/or image data.
• Experience with data processing, feature engineering, and data visualization tools and libraries (e.g., NumPy, pandas, matplotlib, OpenCV).
• Excellent analytical and problem-solving skills, with the ability to critically evaluate research findings and propose innovative solutions.
• Strong communication and collaboration skills, with the ability to work effectively in a multidisciplinary team environment.