Designing and evaluating deep learning models for document understanding and synthetic artifact detection
Applying CNNs, Transformers, and multimodal architectures to real-world datasets
Experimenting with novel architectures, loss functions, and training strategies
Performing error analysis and model interpretability investigations
Contributing to research discussions, technical documentation, and model deployment pipelines

Requirements

Current Master’s or PhD student in Computer Science, Electrical Engineering, or related field
Strong foundation in machine learning and computer vision
Experience with CNNs, Transformers, or related deep learning architectures (LLM or multimodal experience is a plus)
Strong Python programming skills. Hands-on experience with PyTorch or TensorFlow
Demonstrated problem-solving ability and experience running technical or research projects
Clear written and verbal communication skills

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

deep learningmachine learningcomputer visionCNNTransformersmultimodal architecturesPythonPyTorchTensorFlowerror analysis

Soft Skills

problem-solvingcommunication

Certifications

Master’s degreePhD