Ecological Modeller

About the Position

DTU is seeking a highly motivated and experienced researcher to fill the position of Tenure Track Assistant Professor in Ecological Modelling. The successful candidate will be expected to contribute significantly to the development and applications of advanced process-based models for coastal ecosystem research, conservation and management.

The position is part of DTU's Tenure Track program, which offers a unique opportunity for talented researchers to advance their careers and take on leadership roles in their field.

• Main Responsibilities: Develop and apply advanced process-based models to address key aspects of coastal ecosystem functioning, analyze interactions between pressures, coastal habitats and environmental conditions, advance our understanding of how food production interacts with coastal ecosystems affected by human pressures, improve models and decision-support tools for coastal ecosystem management and restoration, and predict impacts of different management strategies under present and future conditions.
• Teaching and Supervision: Teach and supervise at BSc and MSc level, co-supervise PhD students and participate in the section's team providing advice to public management and industry.

Requirements and Qualifications

The ideal candidate will have a strong background in ecological modelling with a focus on coastal ecosystems, pressure-impact relationships, and aquatic nature restoration. Key qualifications include experience with application and development of process-based ecological models, strong skills in scientific programming (Python, R, Matlab, etc.), preferable experience with model and software development, experience in data analysis and statistical tools, publications in high-ranking journals, and good collaboration and communication skills.

Assessment Criteria

Candidates will be assessed against the main responsibilities, requirements and qualifications stated above and the following general criteria: experience and quality of teaching, research experience, research vision and potential, international impact and experience, societal impact, innovativeness including commercialization and collaboration with industry, leadership, collaboration and interdisciplinary skills, and communication skills.