Research Scientist - AI Applications

About the position

We are seeking a Research Scientist to drive innovation in advanced information processing and explore diverse AI applications. This role focuses on algorithm development, problem formulation, and experimental validation. While a key initial focus involves information processing (such as image denoising), the position emphasizes strong AI fundamentals, disciplined exploration, and the ability to apply technical reasoning to a wide range of problem domains.

Responsibilities

• Design, implement, and evaluate algorithms for information and signal processing, with representative problems such as image denoising for low-signal-to-noise ratio data
• Analyze and model data characteristics (e.g., noise modeling and analysis, signal extraction, or structured data patterns).
• Formulate complex problems using objective functions, regularization, or optimization-based approaches.
• Benchmark novel methods against classical and modern AI baselines using quantitative metrics.
• Explore and develop prototype AI applications for emerging fields, including Photonic Chip Design.
• Perform feasibility studies and develop proof-of-concept implementations for new problem sets.
• Compare alternative architectural approaches and provide data-driven recommendations to the leadership team.
• Work closely with technical leadership to define research problems, execute experiments and refine problem formulations.
• Document assumptions, methods, and results in clear, concise technical reports, internal notes, or draft publications.
• Produce high-quality visualizations and comparative analyses suitable for internal review and external dissemination.

Requirements

• Master’s degree with at least 3 years of relevant experience or PhD in Computer Science, Applied Mathematics, Physics, Electrical Engineering, or a related field.
• A wide range of experience across different models of AI applications, demonstrating the ability to adapt and map various architectures to unique mathematical or computational constraints.
• Solid understanding of optimization, objective-function design, or energy-based modeling.
• Strong background in general signal/information processing.
• Proficiency in Python and scientific ML tools (NumPy, SciPy, PyTorch, or equivalent).