Research Scientist - AI Applications

QCi

1d•Onsite

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.

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).

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.