DTU Tenure Track Assistant Professor/ Researcher in Data Science and Artificial Intelligence - DTU Wind

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DTU Tenure Track Assistant Professor/ Researcher in Data Science and Artificial Intelligence - DTU Wind
Roskilde, Denmark
Job Description

Would you like to become part of the solution towards green energy transition, by helping develop the tools and methods for data driven approaches? The Structural Integrity and Load assessment (SIL) section at DTU Wind and Energy Systems is inviting candidates to apply for a Researcher/Assistant/Associate Professor in Data Science and Artificial Intelligence (depending on qualifications), considering both onshore, bottom-fixed, and floating offshore wind turbines.   

Responsibilities and qualifications
You will be part of the SIL team that works on: 

  • Risk, Reliability Engineering and RAM (reliability, availability, maintainability) analysis​, 
  • Probabilistic design, assessment and uncertainty quantification, 
  • Data, Digitalisation, AI/ML and decision support systems (I.e cyber-physical systems)​, 
  • Materials and monitoring, 
  • Service life integrity assessment and end of life scenarios​,
  • Project valuation and Life Cycle Cost modelling and
  • Wind farm-wide operational strategies​.

As a Data Science and Artificial Intelligence Specialist, you are expected to leverage your expertise in these domains to drive innovative research activities within the wind energy sector. Your role centers on harnessing the power of data science, machine learning and AI to enhance lifetime assessment and achieve cost reduction for wind turbine structures and key components.

This entails developing cutting-edge models that can accurately capture and utilize uncertain relationships and patterns present in wind energy systems. This is done in collaboration with the section and the department, in national, EU and industry sponsored projects. Indicative projects include the EU-funded TWAIN and TailWind. 

Your primary responsibilities will be:

  • Advanced Machine Learning Models: Crafting sophisticated machine learning models, including deep learning, sequence models, reinforcement learning and generative models, specifically tailored to address challenges in wind energy systems. Your models will enable more accurate predictions and decision-making.
  • Uncertainty Quantification: Application of advanced statistical and computational techniques, such as Bayesian inference and probabilistic modeling, to effectively quantify and analyze uncertainties within wind turbine components and operations. Your work will ensure robust and reliable assessments under varying conditions.
  • Surrogate Modeling with ML: Employ ML-driven surrogate modeling techniques to efficiently approximate complex simulations, significantly reducing computational resources while maintaining high-fidelity results. This will streamline analysis processes and enhance efficiency.
  • Data science: extract useful insights from data through application of advanced statistical and data modelling techniques
  • Physics-informed Machine Learning: fuse the information from physical relationships and measured data to develop accurate and well-generalizable data-driven models;
  • AI-Driven Scenario Simulation and Optimization: Lead the development of AI-driven scenario simulation and optimization techniques, empowering efficient decision support systems for wind energy operations and maintenance, while harnessing the power of artificial intelligence to enhance decision-making processes.
  • Research Excellence: Publish your research findings in prestigious international journals and present your work at renowned international conferences.
  • Grant Application: Contribute to the formulation of grant applications for national and European calls to secure funding for cutting-edge research.
  • Academic Contribution: Actively participate in the academic community by teaching and supervising BSc and MSc students at DTU. You will also have the opportunity to support the supervision of PhD students within the department.

The following qualifications will be beneficial:

Essential qualifications:

  • Educational Background: A background in mechanical, civil, or electrical engineering with a strong focus on applied mathematics or data analytics within the context of machine learning and artificial intelligence applied to wind energy systems.
  • Machine Learning and AI Expertise: Direct experience and proficiency in applying machine learning and artificial intelligence techniques to solve complex challenges in the wind energy sector, particularly in areas related to structural integrity, load assessments, and reliability analysis of wind turbine components and systems.
  • Numerical Model Development: Proficiency in developing numerical models and advanced decision support frameworks specifically tailored to enhance machine learning and AI applications for uncertainty quantification and reliability analysis in wind turbine components and systems.
  • Scientific Programming: Strong programming skills in scientific languages such as Python, MATLAB, or C, with a demonstrated ability to apply these skills to develop machine learning and AI models and conduct analyses relevant to wind energy systems.

Desired qualifications: 

  • Advanced Machine Learning Expertise: Demonstrated advanced expertise in cutting-edge machine learning techniques, such as deep learning, reinforcement learning, sequence models and generative models, as applied to wind energy systems.
  • Surrogate Modeling: Experience in utilizing ML-based surrogate modelling techniques to efficiently approximate complex simulations and data in the context of reliability analysis, structural integrity assessment, uncertainty quantification and decision support for wind energy.
  • Data Analytics and Risk-Based Methods: Proficiency in data analytics, risk-based methods, and machine learning applications specific to uncertainty quantification, reliability analysis, and decision support systems in the wind energy sector. 
  • Research Proposal Development: Demonstrated expertise in formulating and crafting persuasive research proposals tailored to machine learning and AI applications. Proven track record in securing research funding and driving successful grant applications.

You must be responsible for the teaching of courses. DTU employs two working languages: Danish and English. You are expected to be fluent in at least one of these languages, and in time are expected to master both.

As formal qualification you must hold a PhD degree (or equivalent).

You will be assessed against the responsibilities and qualifications stated above and the following general criteria:

  • Experience and quality of teaching 
  • Research experience 
  • Research vision and potential
  • International impact and experience 
  • Societal impact
  • Innovativeness, including commercialization and collaboration with industry
  • Leadership, collaboration, and interdisciplinary skills
  • Communication skills

We offer
We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility. We develop talent by offering a career mentor, state-of-the-art research infrastructure, and postgraduate teacher training.  
Salary and terms of employment 
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. 

Starting date is 1 February 2024 ( or according to agreement ). The position is a full-time position.

The position is part of DTU’s Tenure Track program. Read more about the program and the recruitment process here .

You can read more about career paths at DTU here .

Further information 
Further information may be obtained from Professor Athanasios Kolios, Head of Division, [email protected] .

You can read more about DTU Wind and Energy Systems  at www.windenergy.dtu.dk/English

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark .

Application procedure
Your complete online application must be submitted no later than 30 December 2023 (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:

  • Application (cover letter)
  • Vision for teaching and research for the tenure track period 
  • CV including employment history, list of publications, H-index and ORCID (see http://orcid.org/ )
  • Teaching portfolio  including documentation of teaching experience 
  • Academic Diplomas (MSc/PhD)

Applications received after the deadline will not be considered.

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply.

DTU Wind and Energy Systems is one of the largest and most well-known university department for wind energy in the world with 400 employees. The institute is in the international driving field with a unique integration of research, education, innovation and public / private government service. DTU Wind and Energy Systems has extensive expertise in wind turbine technology, focusing on the impact of loads, structural design and reliability, aeroelastic design and materials.

Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.


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Job Info
  • Job Identification 2755
  • Job Category VIP A
  • Posting Date 12/18/2023, 03:48 AM
  • Apply Before 12/30/2023, 05:59 PM
  • Locations Frederiksborgvej 399, Roskilde, 4000, DK

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