About this role
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