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PhD Student - Structural Health Monitoring for Wind Turbine Rotor Blades - Job Opportunity at EnBW Energie Baden-Württemberg AG

Berlin/Hamburg, Germany
Full-time
Entry-level
Posted: February 28, 2025
Hybrid
EUR 47,000 - 55,000 annual based on German academic researcher standards and industry alignment

Benefits

Comprehensive work flexibility including remote work and workation options for up to 90 days/year
30 days annual vacation
Holiday and Christmas bonus
Profit sharing program
Capital formation benefits
Subsidized public transport ticket
Home office equipment allowance
Comprehensive mentoring program
Device and bicycle leasing options

Key Responsibilities

Develop novel methods for data and model-based early damage detection in wind turbine rotor blades
Define and validate key performance indicators for structural damage detection
Implement machine learning and statistical methods for robust monitoring strategies
Analyze sensor data from operational wind turbines
Collaborate with internal and external experts
Publish at least 3 articles in renowned academic journals
Present findings at professional conferences

Requirements

Education

Master's degree in Mechanical Engineering, Civil Engineering, Electrical Engineering, Computer Science, Mathematics, Physics or related field

Experience

Initial experience in wind turbine dynamics preferred

Required Skills

Python programming Statistical modeling Data analysis Wind turbine dynamics knowledge Structural monitoring expertise Signal processing Machine learning Fluent German and English Team collaboration Independent work style
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Sauge AI Market Intelligence

Industry Trends

The wind energy sector is experiencing rapid growth driven by aggressive renewable energy targets, creating high demand for specialized monitoring solutions Integration of AI and machine learning in wind turbine maintenance is becoming increasingly critical for operational efficiency There's a growing focus on extending wind turbine lifespan through predictive maintenance technologies

Role Significance

Likely part of a specialized research team of 5-8 members within a larger technical department
Entry-level research position with significant potential for academic and industry impact

Key Projects

Development of predictive maintenance algorithms for wind turbine infrastructure Implementation of machine learning models for damage detection Creation of monitoring systems for large-scale wind energy installations

Success Factors

Strong foundation in both theoretical physics and practical engineering applications Ability to bridge academic research with industrial applications Excellent data analysis and programming capabilities Strong scientific writing and presentation skills

Market Demand

High demand with growing emphasis on specialized technical expertise in renewable energy sector

Important Skills

Critical Skills

Machine learning expertise for developing advanced monitoring systems Statistical analysis capabilities for data interpretation Programming skills for implementation of monitoring solutions

Beneficial Skills

Knowledge of wind energy systems Experience with sensor systems Understanding of mechanical structures Project management capabilities

Unique Aspects

Direct application of research to industrial wind energy operations
Combination of academic research with practical industry implementation
Access to real-world wind turbine data and infrastructure

Career Growth

3-4 years for PhD completion with potential for immediate industry position afterward

Potential Next Roles

Senior Research Engineer Technical Team Lead in Renewable Energy Wind Energy Systems Specialist R&D Manager

Company Overview

EnBW Energie Baden-Württemberg AG

EnBW is one of Germany's largest energy companies, actively driving the transition to renewable energy sources

Major player in European energy market with significant investments in renewable infrastructure
Strong presence in Germany with growing international operations
Progressive, research-oriented environment with strong focus on innovation and sustainability
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