Inference and serving stacks (vLLM, Ollama, TGI); quantization (GGUF, AWQ, GPTQ) Research & Evaluation - Experimental design: controls, baselines, ablations, threat-to-validity analysis - Applied statistics: significance testing, confidence intervals, power analysis, effect size - Evaluation methodology: metric design, adversarial and held-out test-set construction, human evaluation protocols - Model analysis: probing, attribution, interpretability, behavior and representation analysis - AI robustness and safety: prompt injection, adversarial inputs, data leakage and memorization. Degrees must be in Computer Science, Software Engineering, Computer Engineering, Data Science, Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Engineering, Computational Linguistics, Quantitative Finance, or Physical or Biological Sciences; all degrees must include coursework in linear algebra, multivariable (vector) calculus, and programming.