Internship Openings

The Computational Sensing Team at MERL is seeking an intern to work on estimation algorithms for single-photon lidar. The ideal candidate would be a PhD student with a strong background in statistical modeling, estimation theory, computational imaging, and/or inverse problems. The intern will collaborate with MERL researchers to design new lidar reconstruction algorithms, conduct simulations, and prepare results for publication. A detailed knowledge of single-photon detection, lidar, and Poisson processes is preferred. Hands-on optics experience may be beneficial but is not required. Strong programming skills in Python or MATLAB are essential. The duration is anticipated to be 3 months with a flexible start date.

The pay range for this internship position will be 6-8K per month.

MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.

Required Specific Experience

• Experience with Python and Python Deep Learning Frameworks.
• Experience with FMCW radar and/or Depth Sensors.

The pay range for this internship position will be 6-8K per month.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that can perform background oriented schlieren (BOS) tomography. The project goal is to utilize both analytical and learning-based architectures to enable the reconstruction of 3D air flows in an indoor setting from BOS measurements coupled with physics informed machine learning. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, differentiable scene rendering, learning-based modeling for imaging, and physics informed neural networks. Preferred skills include experience with schlieren tomography, inverse rendering, neural scene representation, computational imaging hardware, and computationally efficient optimization of PINNs. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

Required Specific Experience

• Experience with differentiable/physics-based rendering.

The pay range for this internship position will be 6-8K per month.

The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research on sensor reasoning models—algorithms that can understand, explain, and act on multi-sensor data (e.g., RF, infrared, LiDAR, event camera) through text-, visual-, and multimodal reasoning. Ideal candidates will be comfortable bridging modern perception (detection/segmentation/tracking) with higher-level reasoning capabilities. Experience with text, visual, and multimodal reasoning is highly preferred. The intern will work closely with MERL researchers to develop novel algorithms, design experiments using MERL’s in-house testbeds, and prepare results for patents and publication. The internship is expected to last 3 months, with a flexible start date from October 2025 onward.

Required Specific Experience

• Reasoning with sensor data: Demonstrated work in text-, visual-, and multimodal reasoning (e.g., VQA over sensor streams, temporal/spatio-temporal reasoning, chain-of-thought, instruction following).
• LLMs & VLMs for sensor perception: Experience aligning or conditioning LLMs/VLMs on sensor outputs (e.g., point clouds, radar heatmaps, BEV features).
• Perception foundations: Solid understanding of state-of-the-art transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) frameworks
• Datasets & evaluation: Hands-on experience with open large-scale multi-sensor datasets (e.g., nuScenes, Waymo Open Dataset, Argoverse) and open radar datasets (e.g., MMVR, HIBER, RT-Pose, K-Radar). Ability to design reasoning-centric benchmarks (e.g., QA over multi-sensor inputs, temporal prediction).
• Proficiency in Python and deep learning frameworks (PyTorch/JAX), plus experience with GPU cluster job scheduling and scalable data pipelines.
• Proven publication record in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML (or equivalent).
• Knowledge of sensor (RF, infrared, LiDAR, event camera) fundamentals; for radar, familiarity with FMCW, MIMO, Doppler signatures, radar point clouds/heatmaps, and raw ADC waveforms.
• Familiarity with MERL’s recent radar perception research, e.g., TempoRadar, SIRA, MMVR, RETR.

The pay range for this internship position will be6-8K per month.

The Computational Sensing team at MERL is seeking a highly motivated PhD student for an internship focused on uncertainty quantification (UQ) in computational modeling of physical systems. The goal of this project is to advance the methodology and practice of UQ, with a focus on generative models, reduced-order stochastic models, and optimal sensor placement for Bayesian inverse problems. The research will draw upon foundational ideas and techniques in applied mathematics and statistics for applications in wave propagation, fluid dynamics, and more generally high-dimensional systems. The ideal candidate will be a PhD student in engineering, applied mathematics, computer science, or related fields with a solid background and publication record in any of the following areas: generative models, stochastic modeling, dimensionality reduction, Bayesian inference, optimal experimental design, and tensor methods. Programming skills in Python or MATLAB are required. Publication of the results obtained during the internship is expected. The duration is anticipated to be at least 3 months with a flexible start date.

The pay range for this internship position will be $6-8K per month.