Researcher with expertise in LiDAR, AI, and Ecology

This role requires a researcher with specialized expertise in LiDAR technology, artificial intelligence (AI), and ecology. The ideal candidate should have a strong understanding of LiDAR systems, including data acquisition, processing, and analysis techniques relevant to ecological studies. Proficiency in AI methods such as machine learning, computer vision, or data modeling is essential to interpret complex ecological data and develop predictive models. A solid foundation in ecological principles and environmental science is necessary to contextualize LiDAR and AI outputs within biological and environmental frameworks. The position likely involves interdisciplinary research combining remote sensing technologies with advanced computational approaches to address ecological questions. Skills in programming languages commonly used in AI and data analysis, such as Python or R, and experience with relevant software tools for LiDAR data processing would be advantageous. The researcher should be capable of designing experiments, managing large datasets, and collaborating across scientific domains. While specific experience and education levels are not provided, the combination of technical expertise in LiDAR and AI with ecological knowledge suggests a candidate with graduate-level education in fields such as environmental science, ecology, computer science, or geospatial technologies, complemented by practical research experience. Strong analytical skills, problem-solving abilities, and familiarity with scientific research methodologies are implied requirements for success in this role.