Tenure Track Assistant Professor in Decision Analytics for Wind Energy Operations

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). 

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