GE Vernova
Bengaluru, Karnataka, India Posted 4d ago
GE Vernova is seeking a highly motivated and talented individual to join the Advanced Research business as an Advanced Research Intern in the field of Artificial Intelligence.
Key Responsibilities
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.
Requirements
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.
