5-7 years in machine learning or related fields with a focus on geospatial applications.
Ph.D. in Computer Science, Machine Learning, Operations Research, or closely related domain.
Experience with deploying geospatial ML models or generative models in practical scenarios.
Background in collaborative projects that integrate ML with ecological, architectural, or design principles.
Familiarity with GIS tools and remote sensing technologies is advantageous.
Responsibilities
Develop ML models for geospatial inference of ecosystem metrics.
Refine deep generative models and reinforcement learning algorithms for design.
Contribute to decision frameworks using procedural generation with ML optimization.
Collaborate with ecologists and data scientists on generative design integration.
Align design outcomes with ecological performance metrics like biodiversity.
Document technical processes and validate models with empirical data.
Benefits
Work within a multidisciplinary team dedicated to innovative ecosystem design.
Opportunities for ongoing research and development in emerging fields.
Engagement with advanced geospatial AI technology impacts on real-world applications.
A culture that supports collaboration across diverse domains like ecology and architecture.
Full Job Description
Key Responsibilities
Develop machine learning models for geospatial inference of key ecosystem metrics, leveraging geospatial AI to synthesize environmental data into actionable parameters for ecosystem design and simulation.
Develop and refine advanced deep generative models and reinforcement learning algorithms for built-environment design.
Contribute to decision-making frameworks that combine procedural generation with ML and data-driven optimization.
Collaborate with computational ecologists and data scientists to integrate generative design with ecosystem simulation models.
Align design outputs with ecological performance indicators such as species richness and carbon sequestration.
Prepare detailed technical documentation and contribute to model validation using empirical ecological data.
Key Goals and Outcomes
Research and development of high-fidelity Geospatial AI models for the automated inference of ecosystem metrics across varied scales.
Utilize inferred geospatial data to drive the computational synthesis and design of functional, resilient ecosystems.
Establish a robust pipeline for integrating remote sensing and geospatial data into generative design workflows.
Deliver scalable ML frameworks that provide real-time or near-real-time feedback on ecological performance (e.g., carbon sequestration and biodiversity).
Develop innovative design methods that support and enhance ecological processes through data-driven optimization.
Required Experience
Proven experience developing and deploying geospatial machine learning models, deep generative models, or RL algorithms in practical research problems.
Ph.D. or equivalent experience in Computer Science, Machine Learning, Operations Research, or related fields.
Demonstrated experience working in cross-functional teams bridging ML research with ecology, architecture, or design.
Preferred Experience
Experience with GIS tools and remote sensing technologies for geospatial analysis.
Prolific corpus of digital or physical expressions rooted in process-driven research and design.
Industry experience combined with a background in leading research and producing striking work.
Technical Skills
Commitment to Nature-centric principles and a willingness to integrate technology and ecology.
Enthusiasm for pushing boundaries in design and science with innovative thinking.
Self-directed with an aptitude for nurturing collaborative teamwork across disciplines
