MATERIAL & ADDITIVE MANUFACTURING ENGINEER (F/M/D)

  • Full Time
  • ADDITIVE MANUFACTURING
  • GERMANY
  • Posted 2 weeks ago
  • Employer will not sponsor applicants for employment visa status; Very good German and English speaking and writing skills are essential

Company Details

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Job Description

At Boeing, we innovate and collaborate to make the world a better place. From the seabed to outer space, you can contribute to work that matters with a company where diversity, equity and inclusion are shared values. We’re committed to fostering an environment for every teammate that’s welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us.

We are currently looking for a Material & Additive Manufacturing Engineer (F/M/D) for our office in Munich, Germany.

Responsibilities:

  • Focus primarily on supporting the Boeing Research & Technology team to apply data science in support of Additive Manufacturing (AM) development projects
  • Apply data science to different aspects of the AM and other manufacturing processes:
  • Develop schema and structure for in-process monitoring data capture
  • Correlate process monitoring data with analytical data from material testing
  • Research novel approaches for in-process monitoring and analysis
  • Development of algorithms for image recognition and defect predictions
  • Process and analyze data from multiple sources such as optical sensors, thermal imaging, acoustic sensors, CT scans and other
  • Apply Machine Learning principles for process improvements
  • Solve AM specific challenges with large data capture, processing and analysis
  • Scope projects to develop future data supported qualification approach of AM components
  • Present complex analytical results in a clear and simple manner to team members and stakeholders
  • Day to day management of different projects focused on AM process engineering beyond the data science aspect (AM material and process development)

Employer will not sponsor applicants for employment visa status.

Basic Qualifications (Required Skills/Experience):

The ideal candidate will demonstrate enthusiasm for additive manufacturing with new approaches to data analysis, machine learning, enjoys work in multi-disciplinary and diverse team and has:

  • Very good knowledge of data analysis, visualization, modelling (5 years or PhD +2 years)
  • Proven application of advanced analytical and statistical methods for manufacturing process
  • Good knowledge and experience with standard machine learning techniques (clustering, classification, regression, random forest, etc.)
  • Experience with computer vision, image processing and/or other process monitoring inputs
  • Knowledge of large data management and processing
  • Knowledge of Machine Learning approaches to process improvement
  • Understanding of big data technologies, analytics engines, reinforcement and deep learning techniques
  • Passion for Additive Manufacturing process as a whole
  • Very good German and English speaking and writing skills are essential

Important information regarding this requisition: This requisition is for a locally hired position in Germany. CANDIDATES MUST HAVE CURRENT LEGAL AUTHORIZATION TO WORK IMMEDIATELY IN GERMANY. BOEING WILL NOT ATTEMPT TO OBTAIN IMMIGRATION AND LABOUR SPONSORSHIP FOR ANY APPLICANTS. Benefits and pay are determined at the local level and are not part of Boeing U.S. based payroll.

Relocation: This position does not offer relocation. Candidates must live in the immediate area or relocate at their own expense.

Experience Level

  • Individual Contributor

Contingent Upon Program Award

  • No, this position is not contingent upon program award

Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.

Tagged as: data science, machine learning, big data technologies, deep learning techniques

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