The Dependability and Automation Research in Cyber-Physical Systems (DARES) Group, part of the Dependable Systems Cyber Laboratories at Tampere University, Finland, is pleased to announce the opening of a Postdoctoral Researcher position on “condition monitoring focusing on digitalization and artificial intelligence (AI) for critical energy infrastructures.” This offers an exciting opportunity to engage in innovative research at the intersection of digital technologies, artificial intelligence, machine learning, and energy system resilience, contributing to the development of smarter, safer and more secure energy infrastructures that are essential for the sustainable future.

Job Description

As the landscape of critical energy infrastructures evolves towards more digitized, interconnected, and intelligent systems, it encounters unprecedented challenges that span both operational and cybersecurity domains. These infrastructures, essential for global energy sustainability, are increasingly susceptible to sophisticated physical and cyber threats that compromise their safety, security, and resilience. In response to these challenges, this project is at the forefront of industrial automation and condition-based maintenance, leveraging digitalization and Artificial Intelligence (AI) to enhance the intelligent condition monitoring of critical energy infrastructures. It builds upon the existing foundation where critical energy assets, such as fleets of wind turbines and photovoltaic (PV) modules, are equipped with a diverse array of sensors and IoT (Internet of Things) devices. These instruments are pivotal in the collection of real-time data concerning the operational status of each asset. Utilizing advanced AI and machine learning approaches, the project seeks to automate the intricate processes of detecting and identifying anomalies indicative of physical faults or cyber-attacks within these assets. A significant challenge lies in developing models that are not only accurate and efficient in their monitoring capabilities but also possess the ability to generalize across various systems within a fleet. These models must be capable of adaptive learning, integrating new data streams and historical incident data to continuously refine and enhance their diagnostic and predictive capabilities. This dynamic approach to condition monitoring is crucial for enabling timely, informed decision-making and the immediate mitigation of any identified issues, thereby ensuring the operational reliability, efficiency, and safety of critical energy infrastructures. Through this project, the postdoctoral candidate will contribute to pioneering efforts in improving the sustainability and resilience of energy systems, ensuring they meet the demands of a rapidly evolving energy landscape.

The exact direction of the research is determined by the project tasks. However, both the research focus and responsibilities can be customized to align with the candidate’s skills, experience, and interests. The feasibility and efficacy of the solutions developed in this project will be assessed, considering representative cases of renewable energy systems provided by industry partners.

This position also involves collaboration within a multidisciplinary team to push the boundaries of current knowledge and technology in this field, publishing research findings in reputable journals, presenting at international conferences, and possibly including a few months of mobility to a Canadian partner during the research stages for collaborative work.

Requirements

Applicants are expected to meet the following criteria:

• Hold a Ph.D. degree in engineering, computer science, applied mathematics, or a closely related field, with a specialization in condition monitoring, machine learning (ML), and/or artificial intelligence (AI).
• Have a strong background and technical expertise in statistics/mathematics, data science/AI/ML, ideally within the contexts of energy and cyber-physical systems.
• Extensive programming experience with Python is a must, and additional programming experience with PySpark, MATLAB, or R is a plus.
• Relevant publications at leading conferences in machine learning and/or reputable journals are a strong plus.
• Proficiency in both written and spoken English is necessary. Proficiency in Finnish is not required.

Tampere University is a unique, multidisciplinary, forward-thinking, and evolving community. Our values are openness, critical thinking, diversity, learner-centredness, courage, erudition, and responsibility. We hope you can embrace these values and promote them in your role.

We offer

The position will be for a period of 2 years, with a possibility for extension. The starting date is preferably 01.06.2024 or as mutually agreed upon by both parties.  A trial period of six (6) months applies to all our new employees.

The salary will be based on both the position requirements and the employee’s personal performance in accordance with the Salary system of Finnish universities. According to the criteria applied to teaching and research staff, the position of a postdoctoral research fellow is placed on level 5-6 of the job requirements scale. In addition to the basic salary, a supplementary salary will be paid according to personal performance, depending on the selected candidate’s qualifications and experience. Typical starting salary is 3600 – 4200 euros per month in total.

We offer you the opportunity to join our dynamic and innovative international community. As a member of staff at Tampere University, you will enjoy a range of competitive benefits, such as occupational health care services, flexible work schedule, versatile research infrastructure, modern teaching facilities and a safe and inviting campus area as well as a personal fund to spend on sports and cultural activities in your free time. Please read more about working at Tampere University. You can also find more information about us and working and living in Tampere by watching our video:  Tampere Higher Education Community – our academic playground

Tampere is the largest inland city in Finland and is considered a major academic hub in the Nordic countries. The region is home to a vibrant, knowledge-intensive entrepreneurial community, and the city is an industrial powerhouse with a rich cultural scene and a reputation as a centre of Finland’s information society. Additionally, Finland is known for being one of the most stable, free, and safe countries in the world, based on various ratings by prominent agencies. Tampere is surrounded by stunning nature, with forests and lakes offering countless opportunities for easy-to-access outdoor adventures and refreshment throughout the year.

Read more about Finland and Tampere:

Visit Finland
This is Finland
Ministry of Economic Affairs and Employment: Welcome to Finland
Visit Tampere

How to apply

Please submit your application through our online recruitment system. The closing date for applications is 01 April 2024 (at 23.59 EEST / UTC +2).

Please write your application and all accompanying documentation (listed below) in English and combine them into a single PDF file named “LastnameFirstname.pdf”. Please note that other file formats will not be accepted.

Applications should include the following documents:

• A cover letter (maximum length 2 pages) explaining your background, motivation for pursuing a research career, rationale for applying for the position, and reasons you are particularly suited for this role.
• A curriculum vitae (freestyle)
• A full list of your publications. Please clearly indicate (in bold) up to ten (10) of your most important publications.
• A copy of Ph.D. degree certificate in its original language. Provide an official translation if the original language is not English.
• The contact details of up to two referees. References will not be contacted during the initial review process.

For more information, please contact:

Assistant Professor, Group Leader Hamed Badihi, hamed.badihi(at)tuni.fi