Responsibilities

• Design and deliver hands‑on deep learning and AI training sessions using real‑world scientific and biomedical datasets
• Develop, maintain, and continuously improve training materials, including notebooks, scripts, exercises, and technical documentation
• Teach core deep learning concepts such as neural networks, convolutional neural networks (CNNs), model training, optimization, and evaluation
• Support and instruct on the use of modern deep learning frameworks (e.g., PyTorch, TensorFlow, Keras) in GPU‑enabled HPC environments
• Guide users on best practices for running AI workloads on shared HPC systems, including job scheduling, performance optimization, and resource utilization
• Collaborate with HPC and scientific computing teams to ensure training content aligns with current infrastructure, policies, and tools
• Provide post‑training support through office hours, troubleshooting, and consultation for ongoing research projects
• Keep training curricula current with advances in AI, deep learning tooling, and evolving research priorities

Qualifications

• Advanced degree (PhD or MD) in Computer Science, Data Science, Computational Biology, Bioinformatics, or a related discipline
• Strong hands‑on experience developing and training deep learning models
• Practical experience working in Linux‑based HPC environments, including GPU computing
• Demonstrated experience delivering technical training, workshops, or instructional content to scientific or technical audiences
• Excellent communication skills with the ability to explain complex technical concepts to diverse audiences

Preferred Experience

• Experience supporting biomedical, life sciences, or federally funded research initiatives
• Familiarity with large‑scale research computing environments and HPC workflows
• Experience building reproducible training materials using tools such as Jupyter notebooks and Git‑based repositories
• Knowledge of data management, security, and compliance considerations in regulated research environments