Postdoc in Data Driven Modeling for Monitoring Wind Turbine Bering Operation (2024-221-102697)
Vacant position
Postdoc in Data Driven Modeling for Monitoring Wind Turbine Bering Operation (2024-221-102697)
Vacant position
Postdoc in Data Driven Modeling for Monitoring Wind Turbine Bering Operation (2024-221-102697)
Job description
We are is seeking a Post Doc candidate for a project in collaboration with Siemens-Gamesa; focused on the development and application of data driven modeling techniques for monitoring the health of the main bearing in wind turbines. This innovative research aims to enhance the operation of next-generation offshore wind turbines through the development of a novel condition monitoring system. The project is founded by Innovation Found Denmark.
Responsibilities: In the project two main approaches are compared. One based on black/gray box machine learning methods and another one on gray/white box data driven methods. The Post Doc will be working with the latter.
The model structure will be founded on first principles models with parameters that ideally possess physical significance. Parameters that are less known or unknown will need to be estimated using Maximum Likelihood or similar estimation techniques. A well-defined model will primarily be characterized by specific turbine and wind condition parameters. The model is used for condition monitoring and fault detection using methods focusing on statistical methods using residual generation and Kalman filtering.
Qualifications:
Phd and master's degree in, Engineering, Data Science, Computer Science or a related field with a strong foundation in physical and data driven modeling, statistics and stochastic processes. Experience in control engineering, signal processing, data analysis, or related areas. Proficiency in programming languages commonly used in data driven modeling and statistical detection methods, such as Matlab, Python or R. Excellent analytical and problem-solving skills. Ability to work independently as well as collaboratively in a team-oriented environment. Strong communication skills, both written and oral, are essential.
The project will be conducted under the auspices of the Learning and Decisions research group. This group specializes in developing control and decision-making strategies for autonomous systems and infrastructures, integrating physical models with pervasive data. It combines three key areas of research: optimization(encompassing multi-objective optimization, dynamic programming, and reinforcement learning), safety and resilience evaluation, and the secure, privacy-preserving implementation of control algorithms You may obtain further professional information from Prof. Rafal Wisniewski,, email:raf@es.aau.dk
Qualifications requirements:
Appointment as Postdoc presupposes scientific qualifications at PhD–level or similar scientific qualifications.
The research potential of each applicant will be emphasized in the overall assessment. Appointment as a Postdoc cannot exceed a period of four years in total at Aalborg University.
Responsibilities: In the project two main approaches are compared. One based on black/gray box machine learning methods and another one on gray/white box data driven methods. The Post Doc will be working with the latter.
The model structure will be founded on first principles models with parameters that ideally possess physical significance. Parameters that are less known or unknown will need to be estimated using Maximum Likelihood or similar estimation techniques. A well-defined model will primarily be characterized by specific turbine and wind condition parameters. The model is used for condition monitoring and fault detection using methods focusing on statistical methods using residual generation and Kalman filtering.
Qualifications:
Phd and master's degree in, Engineering, Data Science, Computer Science or a related field with a strong foundation in physical and data driven modeling, statistics and stochastic processes. Experience in control engineering, signal processing, data analysis, or related areas. Proficiency in programming languages commonly used in data driven modeling and statistical detection methods, such as Matlab, Python or R. Excellent analytical and problem-solving skills. Ability to work independently as well as collaboratively in a team-oriented environment. Strong communication skills, both written and oral, are essential.
The project will be conducted under the auspices of the Learning and Decisions research group. This group specializes in developing control and decision-making strategies for autonomous systems and infrastructures, integrating physical models with pervasive data. It combines three key areas of research: optimization(encompassing multi-objective optimization, dynamic programming, and reinforcement learning), safety and resilience evaluation, and the secure, privacy-preserving implementation of control algorithms You may obtain further professional information from Prof. Rafal Wisniewski,, email:raf@es.aau.dk
Qualifications requirements:
Appointment as Postdoc presupposes scientific qualifications at PhD–level or similar scientific qualifications.
The research potential of each applicant will be emphasized in the overall assessment. Appointment as a Postdoc cannot exceed a period of four years in total at Aalborg University.
The application must contain the following:
- A motivated text wherein the reasons for applying, qualifications in relation to the position, and intentions and visions for the position are stated.
- A current curriculum vitae.
- Copies of relevant diplomas(Master of Science and PhD). On request you could be asked for an official English translation.
- Scientific qualifications. A complete list of publications must be attached with an indication of the works the applicant wishes to be considered. You may attach up to 5 publications.
- Dissemination qualifications, including participation on committees or boards, participation in organisations and the like.
- Additional qualifications in relation to the position. References/recommendations.
- Personal data.
The applications are only to be submitted online by using the"Apply online" button below.
Shortlisting will be applied. After the review of any objections regarding the assessment committee, the head of department, with assistance from the chair of the assessment committee, selects the candidates to be assessed. All applicants will be informed as to whether they will advance to assessment or not.
AAU wishes to reflect the diversity of society and welcomes applications from all qualified candidates regardless of personal background or belief.
For further information concerning the application procedure please contact HR department by mailest-st-hr@adm.aau.dk. Information regarding guidelines, ministerial circular in force and procedures can be seenhere.
Wages and employment
Employment is in accordance with the Ministerial Order on the Appointment of Academic Staff at Universities(the Appointment Order) and the Ministry of Finance's current Job Structure for Academic Staff at Universities. Employment and salary are in accordance with the collective agreement for state-employed academics.
Ref number
2024-221-102697