PhD Position in Development of Autonomous Decision Support Systems for Wind Turbines

2 months ago


Kongens Lyngby, Lyngby-Tårbæk Kommune, Denmark Danmarks Tekniske Universitet (DTU) Full time

Project Overview

We are seeking a PhD student to join our team at DTU Wind to develop an autonomous decision support system for wind turbine maintenance. The project aims to enhance the O&M practices of wind turbines by creating a versatile, modular, autonomous decision support system that integrates data acquisition, machine learning, and autonomous reasoning modules. The system will suggest optimal interventions to address faults causing degradation of wind turbines and plant power generation performance.

About the Role

As a PhD student in this project, you will be responsible for conceptualizing and realizing a decision support system tailored to enhance wind turbine O&M practices. Your tasks will include identifying signals or metrics indicating asset health state related to fault/degradation modes, identifying sensor solutions and data sources, and developing tools for automatic analysis of sensor data. You will work closely with researchers and engineers at DTU and external partners, including Vestas and the University of Granada.

Requirements

  • A two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree
  • Experience programming with scientific Python
  • Experience with data science/analytics and machine learning methods
  • Experience with wind energy topics
  • Ability to work efficiently in a project team and take responsibility for your own research goals

What We Offer

As a PhD student in this project, you will have the opportunity to work on a cutting-edge research topic, collaborate with an international team of experts, and contribute to the development of advanced decision support systems for wind turbine operations and maintenance. You will also have access to advanced training in European universities and industry, participate in scientific discussions within the Marie Skłodowska-Curie Action Doctoral Networks context, and benefit from a competitive salary and appointment terms.



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