Hardware Machine Learning PhD Research Internship

📋 Description

• Architect and develop an ML research project in a real-world trading env.
• Collaborate with hardware engineers to implement and deploy ML inference.
• Evaluate NAS, ML systems, and quantization for improvements.
• Present project findings to the team on ML acceleration.
• Learn hardware design fundamentals from RTL developers and apply them.
• Evaluate research under real-world performance, cost, and impact.

🎯 Requirements

• PhD enrollment in EE, CS, Physics, or related field.
• Understand hardware constraints and trade-offs for mapping ML to FPGAs/ASICs.
• Experience with VHDL/SystemVerilog, HLS, or ML-to-hw tools (hls4ml, FINN, Vitis AI).
• ML fundamentals: NN architectures, inference optimization, quantization; PyTorch/TensorFlow.
• Proficiency in Python for tooling, testing, and simulation.
• Strong communication and cross-disciplinary collaboration.

🎁 Benefits

• Discretionary bonus and benefits; paid leave and insurance.