Computer Scientists – Deep Learning for 3D Reconstruction in Sustainable AgricultureAn innovative research organization is seeking two skilled Computer Scientists to join its applied research team focused on advancing sustainable agriculture through cutting-edge AI and 3D modeling technologies. This is a full-time, permanent role offering the chance to contribute to impactful projects in a fast-paced, start-up-like environment centered on scientific exploration and practical innovation.Role Overview :You will be involved in designing, validating, and optimizing deep learning models for 3D reconstruction , with direct application to real-world challenges in agriculture. This is a hands-on research and development position where creativity, technical expertise, and scientific rigor are equally valued.Key Responsibilities :Research & Development : Design, develop, and improve algorithms for 3D modeling and depth estimation using deep learning techniques.Collaboration : Communicate technical findings effectively with team members through documentation, code reviews, and meetings.Scientific Contribution : Support the preparation of academic publications and presentations for conferences in the field.Project Execution : Manage the end-to-end development cycle — from prototyping and testing to deployment and iteration — of AI models and software components.Required Qualifications :Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related discipline.Minimum of 1 year experience with deep learning-based dense depth estimation and 3D reconstruction .At least 3 years of hands-on experience with SLAM (Simultaneous Localization and Mapping) and related 3D reconstruction techniques.Proficiency in Python and popular ML / data processing libraries (e.g., PyTorch, TensorFlow / Keras, NumPy, SciPy, OpenCV, Pillow).Strong problem-solving abilities and a solid foundation in mathematics.Ability to write high-quality technical documentation and communicate effectively in a collaborative setting.Preferred Qualifications :Background in geometry, statistics , and the mathematical foundations of machine learning .Understanding of supervised, unsupervised, and self-supervised learning techniques.Familiarity with state-of-the-art methods in 3D deep learning , including topics such as SLAM, SfM (Structure-from-Motion), monocular depth estimation, point clouds, and camera parameter estimation.This is an exciting opportunity for individuals passionate about applying AI to solve complex, real-world problems in sustainability and technology.J-18808-Ljbffr
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