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Embedded Systems Internship Topics

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Embedded Systems Internships with Accommodation

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Showing 1–10 of 10 internship topics
Neural Network Optimization for Edge Devices
Interns will research and implement techniques for compressing and quantizing deep learning models to run efficiently on automotive microcontrollers and edge devices. This includes exploring model pruning, knowledge distillation, and low-precision inference methods while maintaining real-time performance for safety-critical applications.
AI Automotive Embedded Systems ResearchView internship →
Real-time Object Detection for Autonomous Vehicles
Interns will develop and optimize embedded computer vision algorithms for detecting pedestrians, vehicles, and obstacles in real-time using automotive-grade hardware. Work will involve implementing YOLO, SSD, or similar architectures with focus on latency minimization and resource constraints.
AI Automotive Embedded Systems ResearchView internship →
Hardware-Software Co-design for AI Inference
Interns will explore the design and optimization of custom hardware accelerators (FPGA/ASIC) integrated with embedded software for automotive AI workloads. This includes profiling AI algorithms, identifying bottlenecks, and architecting solutions that balance performance, power consumption, and cost.
AI Automotive Embedded Systems ResearchView internship →
Sensor Fusion and Multi-modal Learning Systems
Interns will research embedded sensor fusion techniques combining camera, LiDAR, and radar data using machine learning models optimized for automotive platforms. Focus will be on designing robust, low-latency fusion algorithms that operate within vehicle compute budgets.
AI Automotive Embedded Systems ResearchView internship →
Safety and Robustness Testing for AI-based Autonomous Systems
Interns will develop testing frameworks and methodologies for validating AI models in embedded automotive systems, including adversarial robustness evaluation, failure mode analysis, and ISO 26262 compliance verification. Work includes creating synthetic datasets and edge-case simulations for safety-critical scenarios.
AI Automotive Embedded Systems ResearchView internship →
Task Scheduling Algorithm Optimization
Interns will research and implement improvements to real-time task scheduling algorithms (Rate Monotonic, Earliest Deadline First, etc.) for embedded systems. They will analyze scheduling performance metrics, develop optimizations for multi-core processors, and conduct comparative benchmarking studies on various RTOS platforms.
Real-Time Operating System Optimization ResearchView internship →
Memory Management and Cache Optimization in RTOS
Interns will investigate memory allocation strategies, cache coherency protocols, and garbage collection techniques optimized for real-time constraints. The focus will be on reducing memory fragmentation, minimizing context-switching overhead, and ensuring predictable worst-case execution times in embedded systems.
Real-Time Operating System Optimization ResearchView internship →
Interrupt Handling and Latency Reduction
Interns will analyze interrupt service routine (ISR) optimization techniques and develop methods to reduce interrupt latency in real-time systems. Research will include ISR prioritization, nested interrupt handling mechanisms, and latency analysis tools specific to resource-constrained embedded platforms.
Real-Time Operating System Optimization ResearchView internship →
Power-Aware Real-Time System Design
Interns will explore dynamic voltage and frequency scaling (DVFS), sleep state management, and energy-efficient scheduling algorithms for RTOS environments. The research will focus on maintaining real-time guarantees while optimizing power consumption in battery-powered and energy-constrained devices.
Real-Time Operating System Optimization ResearchView internship →
Predictability Analysis and Worst-Case Execution Time (WCET) Estimation
Interns will develop and validate tools for analyzing task execution predictability and estimating WCET bounds in real-time operating systems. Research will include static analysis techniques, timing anomalies in modern processors, and validation methodologies for safety-critical embedded applications.
Real-Time Operating System Optimization ResearchView internship →
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