DRAM Architecture Research Intern

The DRAM Systems & Architecture Lab researches and develops next-generation memory architectures that enable future AI, cloud, data center, and high-performance computing platforms. By combining advanced modeling, simulation, AI-enabled workflows, and architectural innovation, the team evaluates emerging memory technologies and defines solutions that improve performance, scalability, and efficiency for future memory systems. As a DRAM Architecture Research Intern, you will develop software frameworks, automation tools, and AI-assisted workflows that accelerate memory architecture research and analysis. You will work closely with architects and researchers to evaluate next-generation memory technologies, build simulation environments, perform performance analysis, and apply advanced AI capabilities to automate architectural studies and design-space exploration. ## Responsibilities - Develop and enhance software frameworks, simulation environments, and automation tools that support DRAM architecture research and evaluation. - Design and implement AI-assisted workflows for simulation generation, experiment automation, data analysis, technical documentation, and engineering productivity. - Analyze memory architectures, including DRAM, LPDDR, DDR6, MRDIMM, CAMM, and emerging memory technologies, using modeling and simulation techniques. - Perform performance, power, bandwidth, latency, and workload characterization studies to identify architectural trade-offs and optimization opportunities. - Collaborate with architects and researchers to document methodologies, present findings, and contribute to future memory technology strategies and research initiatives. ## Minimum Qualifications - 0-2 years of relevant experience through research, internships, academic projects, publications, or engineering experience in computer architecture, memory systems, simulation, AI, or software development. - Currently pursuing a Master's or PhD degree in Computer Engineering, Electrical Engineering, Computer Science, or a related technical field. - Experience programming in Python, C++, or similar languages used for software development, simulation, automation, or data analysis. - Familiarity with computer architecture, memory systems, performance analysis, modeling, simulation, or systems research. - Demonstrated ability to leverage AI-enabled tools to improve productivity, technical analysis, problem-solving, and engineering workflows. - Experience using generative AI platforms such as Microsoft Copilot, GPT, Claude, Gemini, or similar technologies. - Strong analytical, problem-solving, technical communication, and collaboration skills. ## Preferred Qualifications - Experience developing AI agents, AI-assisted automation workflows, Retrieval-Augmented Generation (RAG) systems, or LLM-based engineering tools. - Familiarity with AI workflow orchestration, prompt engineering, agentic workflows, and automation frameworks. - Experience integrating AI capabilities into scientific computing, simulation platforms, data analytics environments, or research workflows. - Knowledge of memory architectures such as DRAM, LPDDR, DDR6, MRDIMM, CAMM, HBM, or emerging memory technologies. - Experience with architectural simulators, performance modeling, workload characterization, benchmarking, or design-space exploration. - Research experience in computer architecture, memory systems, AI infrastructure, high-performance computing, or data center technologies. - Publications, conference presentations, patents, or demonstrated research contributions in relevant technical fields.