Undergraduate AI & Computer Vision Assistant - Student Service

Job Summary

Position Overview Seeking an undergraduate student to support development and testing of AI and image-processing methods for engineering prototypes. The student will work with images, data, probability-based models, and machine-learning tools to help detect, classify, and evaluate physical system states.

What You Will Do

• Develop and test image-processing and computer-vision methods using Python.
• Work with camera images to identify objects, connections, patterns, and incorrect configurations.
• Prepare datasets, label images, extract features, and evaluate model performance.
• Experiment with classical computer vision and machine-learning approaches.
• Analyze uncertainty and probability in detection and classification results.
• Document experiments, results, and technical decisions.
  
  

Who Should Apply

Purdue juniors or seniors in Computer Science, Electrical/Computer Engineering, Data Science, Mathematics, Statistics, Mechanical Engineering, or a related field. You do not need to know every topic listed above. Strong mathematical reasoning, curiosity, and willingness to learn matter most.

Education

0

Experience

Useful Background

• Python programming and comfort working with data.
• Image processing or computer vision, such as OpenCV, filtering, segmentation, feature extraction, or object detection.
• Probability and statistics, including random variables, distributions, conditional probability, Bayes' rule, expectation, variance, and Markov's inequality.
• Linear algebra, including vectors, matrices, transformations, and eigenvalues/eigenvectors.
• Calculus and basic optimization concepts.
• Interest in stochastic processes and Markov chains, including the Markov property, state transitions, and transition probabilities.
• Machine-learning fundamentals such as classification, training/testing data, loss functions, and model evaluation.
• Interest and eagerness to learn and work with a diverse team, including virtual work with collaborators in different industry sectors and time zones, building a hands-on kit for a variety of communities of learners.
  
  

Helpful, But Not Required

• PyTorch, TensorFlow, scikit-learn, NumPy, or pandas.
• Convolutional neural networks, object detection, or image classification.
• Experience with cameras, embedded systems, robotics, or engineering prototypes.
• Coursework in AI, machine learning, computer vision, probability, statistics, signals, or applied mathematics.
  
  

FLSA Status

Non-Exempt