Ph.D. or equivalent experience in Computer Science, Computational Ecology, Systems Biology, or a related field.
Strong programming skills in languages like Python, C++, or similar; experience with frameworks like PyTorch or JAX.
Foundation in modeling techniques (e.g., differential equations, agent-based modeling, Bayesian approaches) for simulating ecological processes.
Experience handling large, complex ecological datasets such as climate data and biodiversity records.
Knowledge of spatial databases, parallel computing, or cloud-based data storage is preferred.
Responsibilities
Research key ecosystem behaviors to develop a conceptual framework for ecosystem modeling.
Lead the modeling of core ecosystem dynamics defined in the conceptualization phase.
Develop quantitative metrics to evaluate ecosystem health and service provision.
Implement models in a computationally efficient way and document them for usability.
Gather and integrate environmental, ecological, and spatial data for model validation.
Conduct data analysis to derive insights for ecosystem model development.
Prepare comprehensive technical documentation for model operation and maintenance.
Benefits
Collaborative work environment with an interdisciplinary team.
Opportunity to contribute to impactful ecosystem research and modeling initiatives.
Regular progress meetings to support growth and adaptation.
High ownership and independence in driving project milestones.
Full Job Description
Key Responsibilities
Conceptualization and Research: Research and identify key ecosystem behaviours and interactions to create a comprehensive conceptual framework for general ecosystem modelling
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.
Ecosystem 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.
Key Goals and Outcomes
Development, deployment, and validation of a general ecosystem model within two quarters of the start date
Required Experience
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
Technical Skills
Python (NumPy/SciPy/pandas), reproducible research workflows, and Git-based version control
High-performance model implementation (vectorization, profiling/optimization); familiarity with PyTorch or JAX
