DSP Researcher

Prisma-Photonics is a rapidly growing startup company, developing the next-generation smart-infrastructure solution based on novel fiber-sensing technology (smart roads, smart cities, perimeters, and grid monitoring, etc.). The company offers an award-winning disruptive solution; a “sensor free” approach to smart infrastructure. The company is VC backed and in the revenues stage.

Combining pioneering technology in optical fiber sensing with state-of-the-art machine learning, we help prevent environmental disasters, protect human lives, and keep critical energy and transportation backbones running smoothly.

We are looking for a talented, self-driven, and hands-on DSP Researcher to join Prisma Photonics’ R&D team.

In this role, you will research and develop next-generation distributed fiber-optic sensing technologies, focusing on signal processing, system modeling, simulations, and experimental validation. Working at the intersection of physics, electro-optics, fiber-optic sensing, and DSP, you will transform complex physical phenomena into robust sensing solutions for real-world applications.

You will tackle challenging physical and algorithmic problems, contribute to cutting-edge sensing products, and collaborate closely with multidisciplinary R&D teams. This role is ideal for someone who thrives in a fast-paced environment, enjoys working across disciplines, and is excited to take ideas from research and lab validation to large-scale deployment.

What You’ll Do:

• Conduct experimental and theoretical R&D for Prisma’s next-generation distributed fiber-optic acoustic sensing systems.

• Develop, implement, and validate DSP algorithms for coherent optical sensing systems.

• Build end-to-end expertise across the full sensing pipeline, including optical signal generation and propagation, coherent detection, signal acquisition, DSP, and advanced data analysis.

• Design and run laboratory experiments to validate new concepts, improve system performance, and support current and future product generations.

• Develop simulations, models, and analysis tools using Python and/or C++ for lab experiments, field data analysis, signal processing, and system-level performance evaluation.

• Work with field experiment data to identify performance gaps, validate models, and improve sensing capabilities.

• Collaborate with R&D teams across disciplines to develop, validate, and integrate new sensing capabilities.

• Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

What You Bring:

• MSc or PhD in Physics, Applied Mathematics, Electrical Engineering, or a related field.

• Strong understanding of DSP methods, system-level modeling, and validation of algorithms using experimental or field data.

• Strong background in one or more relevant domains, such as electro-optics, fiber sensing, optical communications, RF DSP, radar/sonar, ultrasound imaging, or related fields.

• Experience applying DSP methods to physical sensing problems, including modeling, extracting, and interpreting signals from noisy experimental or field-deployed systems.

• Experience with advanced signal processing methods, such as filtering, spectral analysis, FFT-based processing, estimation, detection, or time-series analysis.

• Experience processing large-scale or streaming signal data and developing real-time or near-real-time DSP pipelines.

• Strong programming skills in Python, C++ or similar.

• Excellent analytical and problem-solving abilities.

• Strong communication skills, with the ability to work effectively both independently and as part of a multidisciplinary team.

• Ability to manage multiple tasks and projects in a dynamic R&D environment.

Great to Have:

• Experience applying AI/ML techniques to signal processing problems and/or using AI-assisted tools to accelerate DSP research, simulations, and experimental analysis.

• Hands-on laboratory experience with advanced optical measurement techniques and optical/electro-optical systems.

• Experience with electro-optical noise analysis, including phase noise, detector noise, amplifier noise, SNR, and related performance limitations.

• Experience with GPU acceleration, CUDA, or parallel computing.