Job Description

Position Overview

The AI Scientist will provide specialized, part-time support to the Solar AI-Enabled Optical Wireless System project, with a focus on the design, training, and optimization of lightweight machine learning models that enable SLIPT control, adaptive modulation, and environment-aware prediction. The role is critical during defined phases of the project—such as dataset preprocessing, model prototyping, and AI-in-the-loop benchmarking—where advanced AI expertise is required, but continuous full-time engagement is not essential.

Key Responsibilities

• Develop, train, and optimize lightweight ML models for SLIPT control, adaptive modulation, and environment-aware prediction.
• Support dataset preprocessing and feature engineering, including quality checks, labeling strategy, and reproducible data pipelines.
• Prototype model architectures and training workflows, and perform systematic evaluation (ablation studies, sensitivity analysis, and robustness checks).
• Conduct AI-in-the-loop benchmarking aligned with project milestones, including performance metrics definition and reporting.
• Collaborate with the hardware and systems team to ensure model designs are hardware-aware and compatible with real-world constraints.
• Deliver well-documented code, experiment logs, and concise technical reports aligned with agreed deliverables and acceptance criteria.

Qualifications

• PhD or MSc in Machine Learning, Computer Engineering, Data Science, or a closely related discipline with demonstrated applied experience.
• Strong proficiency in Python-based ML workflows (e.g., PyTorch/TensorFlow), model training, evaluation, and deployment-oriented optimization.
• Experience with lightweight modeling and efficiency techniques (e.g., pruning, quantization, knowledge distillation, or compact architectures) is desirable.
• Ability to work in a deliverable-driven environment with clear documentation and reproducible experimentation.
• Strong communication skills and ability to collaborate effectively across AI, hardware, and systems engineering teams.