Lab Summary: We are an interdisciplinary team with an aim to empower providers, consumers, and clinical researchers. We develop GenAI/LLM-based Health AI technologies for Samsung Health, explore use cases by co-innovating with our partners, and design services through pilots that eventually turn into groundbreaking commercial, consumer-grade products that are used every day. Our work has been internationally recognized with 70+ peer-reviewed publications and 10 design awards.
Position Summary: Samsung Research America Digital Health Team is looking for Ph.D. candidates with solid GenAI/LLM technology background and digital health-related research project experiences in health chatbot, RAG, fine-tuning, evaluation, agentic framework development. Samsung’s unique advantage in consumer electronics market and growing focus on digital health will provide you with unprecedented big data and health analytics challenges.
Position Responsibilities:
- You will work with an agile team to innovate and develop disruptive digital health services and solutions
- Support projects leveraging smart phones, wearables and hearables in health/wellness domain, your work will significantly benefit real-world patients, elderly, physicians and caregivers.
Required Skills:
- Currently pursuing a Ph.D. in computer science or related areas
- Hands-on project experience and tangible past deliverables in GenAI/LLM application domain
- Solid coding skills with prototyping experience in data science and popular machine learning frameworks
- Strong knowledge in ML/AI, signal processing, deep-learning, generative AI
- Experience with cloud infra services like Azure, GCP, AWS
- Good communication/presentation skill and team work spirit
- Publications in top-tier academic conferences and journals
Special Attributes:
- Experience in time-series data analytics, recommendation engine, NLP
- Strong mathematics background, especially statistics
- Turn the analyzed data into actionable insight and/or understandable visualization
- LLM-based chatbot development
- RAG/Fine-tuning of LLM/SLM models
- Auto-evaluation of LLM in health domain
- Agentic framework/application development experience
