DSP Researcher

Description 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.