autonomy.**Key Responsibilities:

• Develop and Implement SLAM Algorithms: Design and optimize state-of-the-art SLAM algorithms that integrate deep learning models for robust and accurate localization and mapping in dynamic envi ronments.Deep- Learning Integration: Appl y deep learning techniques (e.g., CNNs, RNNs, transformers) to improve traditional SLAM systems for better feature extraction, object detection, and semantic understanding.Senso

• r Fusion: Work with multimodal sensor data, including LiDAR, RGB cameras, depth sensors, and IMUs, to enhance SLAM performance under challenging conditions (e.g., low light, motion blur, moving objects).Algor

• ithm Optimization: Focu s on optimizing SLAM systems for real-time performance, scalability, and resource efficiency (e.g., memory, processing power).Testi

• ng and Validation: Desi gn and run extensive tests to validate the robustness, accuracy, and real-world applicability of SLAM systems across different environments (urban, indoor, outdoor, etc.).Colla

• boration: Work closely with cross-functional teams including hardware engineers, software developers, and data scientists to integrate SLAM algorithms into autonomous systems (robotics, drones, AR devices).Resea

• rch & Innovation: Stay up to date with the latest advancements in SLAM, deep learning, and robotics, and contribute to innovative solutions that drive the next generation of autonomous technologies.Docum

• entation & Reporting: Main tain clear documentation for algorithms, tests, and performance metrics, and report progress to the team and stakeholders. **Qualifications:

• Educational Background:

• MSc or PhD in Computer Science, Robotics, Electrical Engineering, or a related field, with a focus on SLAM, computer vision, or machine learning.

• Technical Skills:

• Strong knowledge of SLAM algorithms (e.g., ORB-SLAM, DSO, Visual-Inertial SLAM) and their real-world applications. Profi- ciency in deep learning frameworks (TensorFlow, PyTorch, Keras) and experience applying them to perception and localization tasks.Exper

• ience with sensor fusion techniques for integrating data from LiDAR, cameras, IMUs, etc.Famil

• iarity with 3D reconstruction, point cloud processing, and structure-from-motion (SfM).Exper

• tise in programming languages such as Python, C++, or ROS for algorithm development and integration.Exper **- ience:3+ ye

• ars of experience working with SLAM and/or deep learning in robotics or computer vision applications.Hands**- -on experience with autonomous systems, mobile robots, or drone navigation.Stron

• g problem-solving skills and the ability to develop innovative solutions to challenging technical problems.Desir **- able:Exper

• ience with reinforcement learning or optimization techniques for improving SLAM robustness.Famil**- iarity with ROS (Robot Operating System) and tools for robot simulation and visualization (e.g., Gazebo, RViz).Previ

• ous experience with deployment of SLAM systems in real-world applications.******