You will contribute to our research by focusing on topics such as:
Decision analysis for wind energy operations under uncertainty, including the interaction between assets, markets, grids, and policy constraints
Stochastic optimisation and dynamic decision models for operational strategies (e.g., maintenance planning, market participation, control strategies, lifetime extension)
Risk, uncertainty quantification, and value-of-information perspectives for operational decision making
System representations (e.g., structured model interfaces/ontologies) that make decision variables, causal pathways, assumptions, and uncertainty explicit, enabling consistent coupling of engineering and techno-economic models
Data-driven and physics-informed models simulating system behaviour and providing decision-relevant insights
Translating methods into decision-support prototypes and case studies in collaboration with academic and industrial partners
Your primary responsibilities will include:
Establishing an independent research direction within decision analytics for wind energy operations in coupled systems
Publishing in high-impact journals and conferences and contributing to open, reproducible research outputs where relevant
Contributing to research-driven software development by integrating own research in new tools
Developing external funding proposals (national, EU, and industry) and contributing to large collaborative projects
Teaching and course development at BSc/MSc level, and contributing to DTU’s pedagogical environment (including supervision of MSc and PhD students)
Strengthening collaboration across DTU Wind sections/divisions and with industry, TSOs/DSOs, and other stakeholders when relevant
Contributing to the scientific community and department activities (seminars, peer review, committees as appropriate)
Qualifications The following qualifications are relevant to this post:
Educational background: PhD (or equivalent) in wind/energy engineering, industrial/operations research, applied mathematics, electrical/power systems, or a closely related field, with a clear research focus relevant to energy-system decision making
Decision-theoretic methods: Demonstrated ability with methods such as stochastic optimisation, probabilistic reasoning, Bayesian/statistical modelling, dynamic decision models (e.g., MDP/POMDP-style thinking), or robust optimisation
Uncertainty and risk: Strong understanding of uncertainty quantification, risk metrics, and decision-making under uncertainty in engineering or energy applications
Computational skills: Strong scientific programming skills (e.g., Python/Julia/MATLAB) and experience implementing modelling/optimisation workflows with high standards for reproducibility
Research track record: Publications commensurate with career stage and the ability to formulate and execute an independent research agenda
Teaching potential: Ability and motivation to teach, supervise, and contribute to course development (pedagogical training can be supported at DTU)
Wind-energy operations insight: Experience with wind farm operation/maintenance, lifetime extension, asset management, or operational modelling connected to real constraints and data
Markets and grids: Familiarity with electricity market design, grid constraints, ancillary services, or system operation planning relevant to wind integration
Model coupling and representation: Experience with model integration, structured system representations, digital-twin concepts, or ontology/knowledge-graph-inspired approaches (not required, but beneficial)
Funding and leadership: Experience contributing to competitive research proposals, mentoring students, and collaborating in interdisciplinary or international settings
Industry/policy collaboration: Experience with stakeholders (industry, grid operators, regulators) and an interest in societal impact pathways
Application procedure
Your complete online application must be submitted no later than 31 March 2026 (23:59 Danish time).