PhD scholarship in Development and Implementation of an Autonomous Decision Support System for
4 months ago
The PhD project aims to create a versatile, modular, autonomous decision support system to enhance the O&M practices of wind turbines, focusing on faults that cause degradation of the wind turbine and plant power generation performance. The system will integrate a data acquisition subsystem, a ML-based data analytics module, and an autonomous reasoning module to suggest optimal interventions.
If you are aspiring for a career in industrial R&D or academia, with a passion for the production of low carbon energy, particularly wind energy, and its most intriguing research challenges, then this subject may be of interest to you.
We are seeking a passionate, curious, and enthusiastic new team member committed to addressing climate change by advancing renewable energy systems. We are looking for someone who thrives on solving complex problems, isn't afraid to ask challenging questions, and possesses the determination to pursue innovative solutions in a rapidly evolving field.
DTU Wind invites applications for a position as a PhD student in “Development and Implementation of an Autonomous Decision Support System for Optimized Maintenance in Wind Turbine Infrastructure”, as part of studies on trustworthy decision process implementation in autonomous operations, investigated in IntelliWind. The IntelliWind (Intelligent systems for autonomous Wind power plant operations) project is a Doctoral Network funded by the EU Marie Skłodowska-Curie Actions program (MSCA DN).
In this role, you will have the chance to evolve within academia at DTU Wind, located in Risø, near Roskilde, just 30 minutes from Copenhagen. You will benefit from advanced training in various European universities and industry, participate in scientific discussions within the rich context of the Marie Skłodowska-Curie Action Doctoral Networks context.
You are a committed individual with a strong background in engineering or applied mathematics / computer science, with a keen interest in scientific programming, machine learning and data science. Your curiosity drives you to explore and understand the intricacies of wind energy systems, and your self-motivation pushes you to expand the boundaries of what is currently possible for a better tomorrow.
Responsibilities and qualifications
Your primary tasks will be to:
- Conceptualisation and realisation of a versatile, modular, autonomous decision support system (DSS) tailored to enhance the O&M practices of wind turbines, focusing specifically on faults that may cause degradation of the wind turbine and plant power generation performance.
- Identify signals or metrics that can be indicators of the asset health state related to the fault/degradation modes being studied.
- Identify sensor solutions and data sources that can deliver the signals relevant for quantifying the performance metrics.
- Develop tools to automatically analyse sensor data to evaluate relevant metrics and relate them to other external factors.
The present project will take advantage of collaboration with researchers and engineers at DTU and at external partners in academia as well as the industry. The project includes close collaboration with Vestas and the University of Granada, and envisages external stays in Denmark (Aarhus), and Spain, respectively.
You must have 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. In addition, we expect that you have some level of:
- Experience programming with scientific python
- Experience with data science/analytics and machine learning methods
- Experience with wind energy topics
You also work efficiently in a project team and take responsibility for your own research goals. There are additional criteria for Marie Skłodowska-Curie DNs, that requires that you have:
- Not been awarded a title of PhD (Applicants who have successfully defended their doctoral thesis but not yet formally been awarded the doctoral degree will not be considered eligible.)
- Not resided or carried out your main activity in Denmark, for more than 12 months within the last 3 years.
Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education.
We offer you the opportunity to be a part of IntelliWind, which will not only facilitate sixteen Doctoral Candidates in reaching a high level of technical and project-specific excellence but will also provide you with many opportunities for developing skills that are transferable to a broader landscape of opportunities. You will have the opportunity to visit industry and other academic institutions within the consortium. After completing the program, you will have a thorough understanding of the process from research via innovation to industry implementation and a strong career-defining network.
Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.
Application procedure
Your complete online application must be submitted no later than 30 September 2024 (23:59 Danish time) .
Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file . The file must include:
- A letter motivating the application (cover letter)
- Curriculum vitae
- Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale
You may apply prior to obtaining your master's degree but cannot begin before having received it.
Applications received after the deadline will not be considered.
All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.
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