Oferty pracy w projektach finansowanych przez NCN

Nazwa jednostki: Faculty of Mechanical Engineering and Robotics, AGH University of Krakow – Krakow, Poland, Inne oferty z tego miasta »
Nazwa stanowiska: Doctoral Student Position
Wymagania:

• The ideal candidate will have a Master's degree or equivalent in Mechanical Engineering, Electrical Engineering, Mechatronics, or Computer Science.
• The candidate must apply to and be accepted into the AGH Doctoral School.
• Strong communication skills in written and verbal English are required.
• Proficiency in one or more programming language, especially MATLAB or Python, is required.
• Knowledge of structural health monitoring, wind turbine condition monitoring and fault diagnosis, and signal/data processing will be advantageous.
• Experience with machine learning and statistical time series methods is desirable.
• A demonstrated record of high-quality publications will be advantageous.
• Strong motivation to explore new techniques and concepts is required.
• The candidate should be self-motivated and possess the ability to work both independently and collaboratively.

Opis zadań:

Predictive maintenance has been considered as an effective strategy to reduce operations and maintenance (O&M) costs and improve the availability and efficiency of wind turbines through condition monitoring (CM) and fault detection, prediction and diagnosis. Wind turbine monitoring using data collected by the supervisory control and data acquisition (SCADA) systems has been seen as a cost-effective and wide-scale approach. As a result, much research has employed SCADA data to develop reliable, efficient and cost-effective monitoring systems in recent years. Most solutions are based on statistical methods and machine learning (ML) techniques. A common practice of the statistical and ML-based methods is the use of normal behaviour models (NBMs) developed for specific turbine components.
The research topics of this doctoral student include:
• investigating data-driven solutions for condition assessment of wind turbines without using NBMs;
• trend analysis and trend detection methods for condition monitoring and anomaly detection of wind turbines;
• exploring change-point detection methods for fault detection of wind turbine components;
• studying nonparametric statistical methods for wind turbine condition monitoring and automated fault detection.

Typ konkursu NCN: OPUS – ST
Termin składania ofert: 15 sierpnia 2024, 17:00
Forma składania ofert: formularz rejestracyjny
Warunki zatrudnienia:

The successful candidate will be enrolled in the Doctoral School at AGH University of Krakow.
The total remuneration, including the scholarship from the AGH Doctoral School and the scholarship from the NCN-financed OPUS 26 project, is:
• 6,500 PLN/month before the mid-term evaluation
• 8,500 PLN/month after mid-term evaluation
The maximum duration of the scholarship is 48 months.
The scholarship will be paid from October 1, 2024.

Dodatkowe informacje:

This job will be carried out as part of the NCN-financed OPUS 26 project entitled "Non-classical Approaches for Condition Monitoring and Fault Detection of Wind Turbines".
The application process takes place via the AGH Doctoral School website at https://sd.agh.edu.pl/en/
Registration of candidates (from 01.07.2024 to 06.09.2024) takes place using the e-Rekrutacja system at https://rekrutacja.doktoranci.agh.edu.pl/
When applying, please select:
• Research topic (Topic ID 0868): Statistical methods for condition monitoring and fault detection of wind turbines
• Link to the research topic: https://rekrutacja.doktoranci.agh.edu.pl/ZagadnieniaBadawcze/?pg=Podglad&zb_id=868
• Supervisor: dr hab. inż. Phong B. Dao
• Supervisor’s email address: phongdao@agh.edu.pl

The following documents must be sent to the supervisor's email address (phongdao@agh.edu.pl). Only selected candidates can submit the application to the AGH Doctoral School.
• a cover letter (or motivation letter) describing research experience, interests and goals;
• a CV including GPA, a list of publications and other achievements;
• at least one recommendation letter from former mentors/referees;
• Master’s degree certificate or equivalent, if applicable, original or notarized copy of diploma and final transcript (English translation required);
• Certificates of fluency in English, if available.

Data dodania ogłoszenia: 2024-06-13 11:53:18