Computational Ecologist

OXMAN is a nature-based research and design company based in Manhattan. We incubate ventures and technologies that reimagine the relationship between humanity and the natural world. Working across disciplines—from architecture and ecology to materials science and computation, we develop nature-centric solutions to critical environmental challenges.

Nature provides humanity with services that are critical for survival: the sequestration of carbon, the filtration of water, and the production of the air we breathe. EDEN works to strengthen and regenerate these natural processes by cultivating biodiverse, resilient ecosystems that sustain life for all species—human and non-human alike.

EDEN is a digital design environment for engineering and designing ecosystems, modeling the flows, relationships, and processes that sustain them. We build tools that quantify how landscapes can be engineered to achieve specific performance goals, cooling cities, filtering water, sequestering carbon, and protecting key species, and use them to guide the design of ecologically active sites.

One hectare of well-designed landscape can sequester up to four times the annual emissions of an average home, filter enough water to support thirteen neighborhoods, and reduce ambient temperatures by more than ten degrees. EDEN enables designers to plan intentionally for these outcomes through analysis, simulation, and optimization, turning ecological function into an actionable design parameter.

Our design team works directly with clients to apply these tools toward site-specific goals, from logistics campuses and residential communities to rewilding and climate-resilient developments. Together with our clients, we are designing biodiverse, productive environments that serve both humanity and nature.

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  Conceptualization and Research: Research and identify key ecosystem behaviours and interactions to create a comprehensive conceptual framework for general ecosystem modelling

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  Ecosystem Behaviour Modelling: Leading of the modelling of core ecosystem dynamics and interactions as defined in the conceptualization phase such as plant growth and succession etc.

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  Ecosystem Metrics Development: Development of quantitative metrics to assess ecosystem health, stability, and service provision.

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  Implementation and Documentation: Models will be implemented in a computationally efficient framework with thorough documentation to ensure usability and reproducibility.

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  Help in the gathering and integration of relevant environmental, ecological, and spatial data to underpin model parameters and validate model outcomes.

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  Conduct data analysis to in order to derive key insights necessary to develop ecosystem models and validate model parameters.

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  Technical documentation: Prepare comprehensive documentation outlining model assumptions, data sources, code structure, and operation for using and maintaining the models.

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  Continually communicate with the OXMAN team to ensure review of research, implementation, and seamless integration of the model into their workflows.

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  Participate in regular progress meetings (weekly or biweekly) with the team to review milestones, discuss challenges, and plan next steps.

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  Provide status updates summarizing progress, challenges encountered, and any adjustments to the project plan.

• Development, deployment, and validation of a general ecosystem model within two quarters of the start date

• A Ph.D. or equivalent experience in Computer Science, Computational Ecology, Systems Biology, or a related field.

• Strong programming skills in languages such as Python, C++, or similar, with experience in frameworks like PyTorch, or JAX.

• Strong foundation in modeling techniques (e.g., differential equations, agent-based modeling, network models, Bayesian approaches) for simulating ecological processes or population dynamics.

• Experience handling large and often messy datasets common in ecology (e.g., climate data, remote sensing imagery, biodiversity records). Knowledge of spatial databases, parallel computing, or cloud-based data storage is a plus

• Python (NumPy/SciPy/pandas), reproducible research workflows, and Git-based version control

• High-performance model implementation (vectorization, profiling/optimization); familiarity with PyTorch or JAX

• Ecological modeling methods: agent-based, ODE/PDE, network, and Bayesian/statistical modeling; uncertainty quantification

• Geospatial analytics (GIS; GeoPandas/rasterio/GDAL) and spatial databases (e.g., PostGIS) for integrating environmental and biodiversity data

• Remote sensing and gridded data handling (e.g., xarray; land cover/land use change; climate rasters); comfort with messy real-world datasets

• Clear technical documentation (assumptions, data provenance, APIs) and maintainable code (testing, modular design)

• Systems-level thinker who can translate ecological theory into tractable computational abstractions

• Strong research judgment: literature synthesis, hypothesis framing, and disciplined model validation

• Pragmatic engineer: prioritizes computational efficiency, robustness, and reproducibility over “toy” prototypes

• Comfortable working with uncertainty, noisy data, and incomplete ground truth, typical of ecological problems

• Clear communicator in interdisciplinary teams (design, biology, engineering); proactive stakeholder management

• High ownership: independently drives milestones while aligning work to EDEN workflow integration needs