Computational Ecologist @ OXMAN

Salary Range: 75000 to 225000 (Currency: USD) (Pay period: per-year-salary)

OXMAN is a hybrid Design and R&D company that fuses design, technology, and biology to invent multi-scale products and environments. The fusion of disciplines within our work opens previously impossible opportunities within each domain—allowing design to inspire science and science to inspire design. At OXMAN, we question dominant modes of design that have divorced us from Nature by prioritizing humanity above all else (human-centric design). Although it is design that has caused this rift, we believe that design also offers the greatest opportunity to heal it. We propose a Nature-centric approach that delivers design solutions by, for, and with the natural world, while advancing humanity. In this pursuit, we reject all forms of segregation and instead call for a radical synergy between human-made and Nature-grown environments. This approach demands that we design across scales for systems-level impact. We consider every designed construct a whole system of heterogeneous and complex interrelations—not isolated objects—that are intrinsically connected to their environments. In doing so, we open ourselves up to moving beyond mere maintenance toward the advancement of Nature.

OXMAN is seeking a Computational Research Engineer or Computer Scientist with specialization in Computational Ecology to join an all-star interdisciplinary team of deep thinkers and brilliant makers, leveraging computation as a language for mediation between human-made and Nature-grown environments. This person embodies both technical rigor and creative ingenuity in applying methods from computational ecology toward the development of novel design methods. In this role, they will conduct research related to ecosystem modeling and analysis on how ecosystems are impacted by spatial and temporal variability and effects of external interventions. They will conceptualize and develop methods to model how man-made interventions propagate through ecological networks, affect biodiversity, ecological resilience, and ecosystem services. This includes but is not limited to predictive modelling of ecosystem progression or inference of ecosystem stability from remote sensing data, statistical modelling of ecosystem activity, or agent-based modelling of ecosystem dynamics. Besides modelling of ecosystem activity one of the primary focus of this engagement is to develop a generalized ecosystem model. This generalized model will be integrated in EDEN’s generation, simulation, and optimization workflows to enable evaluation of ecosystem metrics such as the qualitative analysis of provision of ecosystem services.

Responsibilities

Conceptualization and Research: Research and identify key ecosystem behaviors and interactions to create a comprehensive conceptual framework for general ecosystem modelling.
Ecosystem Behavior Modelling: Leading of the modelling of core ecosystem dynamics and interactions as defined in the conceptualization phase such as plant growth and succession etc.
Ecosystems Metrics Development: Development of quantitative metrics to assess ecosystem health, stability, and service provision.
Implementation and Documentation: Models will be implemented in a computationally efficient framework with thorough documentation to ensure usability and reproducibility.
Help in the gathering and integration of relevant environmental, ecological, and spatial data to underpin model parameters and validate model outcomes.
Conduct data analysis to in order to derive key insights necessary to develop ecosystem models and validate model parameters
Technical documentation: Prepare comprehensive documentation outlining model assumptions, data sources, code structure, and operation for using and maintaining the models.
Continually communicate with the OXMAN team to ensure review of research, implementation, and seamless integration of the model into their workflows.
Participate in regular progress meetings (weekly or biweekly) with the team to review milestones, discuss challenges, and plan next steps.
Provide status updates summarizing progress, challenges encountered, and any adjustments to the project plan.

Qualifications

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
Proficiency in Geographical Information Systems (GIS) for spatial analysis, mapping, and geostatistics. Remote sensing knowledge is valuable for deriving land cover and land use changes relevant to built environments
Proficiency in methods of data-driven design optimization is a plus.

Computational Ecologist

OXMAN

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