Student Intern - Research

The Bell Labs Platform ASIC Research lab explores advanced computing architectures and algorithms for next-generation communication systems. In this internship, you will focus on enabling energy-efficient AI and wireless communication workloads through hardware-aware AI algorithms, neuromorphic computing, and domain-specific hardware acceleration, with potential exploration of RISC-V-based accelerators. You will work across AI/ML, wireless signal processing, energy-efficient computing, and hardware/software co-design, developing concepts and running experiments in Python and C. The work may also involve evaluating compute and energy-efficiency trade-offs and contributing to Verilog-based implementations, accelerator microarchitecture, or architecture design studies. You will work closely with Nokia Bell Labs researchers on early-stage research that could influence future intelligent and energy-efficient wireless systems. 

Your responsibilities

Key Responsibilities 

• Explore research ideas related to energy-efficient compute for AI-driven wireless communication systems. 
• Study and evaluate neuromorphic computing approaches and their applicability to wireless workloads. 
• Develop experimental prototypes, models, and benchmarks using Python and C. 
• Explore accelerator concepts, including RISC-V-based acceleration, custom instruction extensions, or hardware/software co-design approaches. 
• Potentially contribute to Verilog implementation, microarchitecture exploration, or architecture-level design and simulation. 
• Compare conventional AI compute approaches with low-power, event-driven, approximate, or neuromorphic compute techniques. 

Your skills and experience

You have

• Enrolled in a Ph.D. or MS Research program at the time of the internship. 
• Strong background in one or more areas such as computer architecture, VLSI/digital design, AI/ML algorithms, wireless communications, signal processing, embedded systems or hardware/software co-design 
• Hands-on programming experience in Python and/or C. 
• Ability to design experiments, analyze results, and iterate on research ideas independently. 
• Strong fundamentals in algorithms, digital systems, and performance/energy trade-off analysis. 
• Availability for a 6-month internship. 

It would be nice if you also have 

• Exposure to RISC-V architecture, instruction-set extensions, accelerator design, or processor microarchitecture. 
• Interest or experience in neuromorphic computing, spiking neural networks, event-driven compute, or low-power AI. 
• Familiarity with energy-efficient computing techniques such as quantization, sparsity, approximate computing, pruning, or workload-specific acceleration. 
• Understanding of RTL design, hardware simulation, or FPGA/ASIC design flows.