Skill

Generative Adversarial Networks (GANs)

Data Science, Analytics and AI/ML

Generative Adversarial Networks (GANs) are a class of machine learning models, introduced by Ian Goodfellow in 2014, in which a generator network and a discriminator network are trained simultaneously in an adversarial process to produce increasingly realistic synthetic data. They have been widely used to generate photorealistic images, enhance image resolution, and create synthetic training data. AI researchers and engineers apply GANs across computer vision, art generation, and data augmentation tasks.

Open roles requiring Generative Adversarial Networks (GANs) (1)

Associate Director, Generative AI

Gilead Sciences

Full-time · United States - California - Foster City · $195,670 – $253,220

This role leads generative AI product development and deployment across Gilead's commercial functions, responsible for developing strategy, managing the roadmap, and ensuring responsible AI governance. It suits experienced data science leaders with pharmaceutical commercial expertise who can bridge technical AI capabilities with business needs while navigating regulatory requirements.

Listed on Gilead Sciences’s careers site · Apply there ↗

Roles that use Generative Adversarial Networks (GANs)

• Data Scientist1

• Deep Learning
• PyTorch
• Neural Networks
• TensorFlow
• Computer Vision
• Autoencoders