Postdoctoral Research Fellow (Condition monitoring focusing on digitalization and artificial in[...]

Phmsociety; Tampere; On-site; EUR 40 000 - 60 000; Full time

Job summary

A leading educational institution in Tampere is seeking a Postdoctoral Researcher to work on condition monitoring focusing on digitalization and AI for critical energy infrastructures. The role involves research on enhancing operational reliability and resilience of energy systems using machine learning and AI techniques. Candidates should hold a Ph.D. and have a strong background in Python programming and statistics. Competitive salary and benefits are offered.

Benefits

Occupational health care

Flexible work schedule

Access to research infrastructure

Personal fund for sports and cultural activities

Qualifications

• Hold a Ph.D. in engineering or related field specializing in condition monitoring.
• Strong background in statistics, data science, or AI, ideally within energy systems.
• Experience with Python; knowledge of PySpark, MATLAB, or R is advantageous.

Responsibilities

• Engage in research on condition monitoring using AI for energy infrastructures.
• Collaborate within a multidisciplinary team to advance knowledge and technology.
• Publish research findings in reputable journals and present at international conferences.

Skills

Python programming

Machine Learning (ML)

Artificial Intelligence (AI)

Data science

Statistics/Mathematics

Education

Ph.D. in engineering, computer science, applied mathematics

Tools

PySpark

MATLAB

R

Job description

Overview

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

Job Description

As critical energy infrastructures evolve toward digitized, interconnected, and intelligent systems, they face challenges spanning operational and cybersecurity domains. These infrastructures are essential for global energy sustainability and are increasingly susceptible to physical and cyber threats that affect safety and resilience. The project centers on industrial automation and condition-based maintenance, leveraging digitalization and AI to enhance intelligent condition monitoring of critical energy infrastructures. It builds on a foundation where assets such as fleets of wind turbines and PV modules are equipped with sensors and IoT devices to collect real-time operational data. Using AI and machine learning, the project aims to automate detection and identification of anomalies indicating physical faults or cyber-attacks. A key challenge is developing models that are accurate and efficient and that generalize across multiple systems within a fleet. These models should enable adaptive learning by integrating new data streams and historical incident data to refine diagnostic and predictive capabilities, supporting timely, informed decision-making and immediate mitigation to ensure operational reliability, efficiency, and safety. Through this project, the postdoctoral candidate will contribute to improving the sustainability and resilience of energy systems.

The precise direction of the research is defined by project tasks, but both the focus and responsibilities can be customized to align with the candidate’s skills and interests. Feasibility and efficacy will be assessed using representative cases of renewable energy systems provided by industry partners.

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

Requirements

Applicants are expected to meet the following criteria:

• Hold a Ph.D. in engineering, computer science, applied mathematics, or a closely related field, with specialization in condition monitoring, ML/AI.
• Strong background in statistics/mathematics, data science/AI/ML, ideally within energy and cyber-physical systems.
• Extensive Python programming experience; experience with PySpark, MATLAB, or R is a plus.
• Publications at leading conferences in ML or reputable journals are a strong plus.
• Proficiency in written and spoken English is necessary. Proficiency in Finnish is not required.

Tampere University emphasizes openness, critical thinking, diversity, learner-centeredness, courage, erudition, and responsibility, and invites candidates to embrace these values in their role.

We offer

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

The salary will be based on the position requirements and the employee’s performance, according to the Finnish universities salary system. The postdoctoral research fellow is placed on level 5-6 of the job requirements scale, with a supplementary salary based on personal qualifications and experience. Typical starting salary is 3600 – 4200 euros per month in total.

We offer the opportunity to join Tampere University’s dynamic international community, including benefits such as occupational health care, flexible work schedule, access to research infrastructure, modern teaching facilities, a safe campus, and a personal fund for sports and cultural activities. Please read more about working at Tampere University. You can also learn about Tampere by watching the video: Tampere Higher Education Community – our academic playground.

Tampere is a large inland city in Finland, known for its academic environment and vibrant community. Finland is regarded as stable and safe, with rich nature and outdoor opportunities year-round.

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 documents in English and combine them into a single PDF file named “LastnameFirstname.pdf”. Other file formats will not be accepted.

Applications should include the following documents:

• A cover letter (maximum 2 pages) outlining your background, motivation for pursuing a research career, rationale for applying, and reasons you are suited for this role.
• A curriculum vitae (freestyle).
• A full list of publications with up to ten (10) highlighted publications.
• A copy of Ph.D. certificate in original language; provide an official translation if not in English.
• Contact details of up to two referees (references will not be contacted during the initial review).

For more information, please contact: Assistant Professor, Group Leader Hamed Badihi, hamed.badihi(at)tuni.fi