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Digital Twin and Machine Learning Research PhD Position - Job Opportunity at Université Paris-Saclay GS Sciences de l'ingénierie et des systèmes

Gif-sur-Yvette, France
Full-time
Entry-level
Posted: February 22, 2025
On-site
EUR 30,000-35,000 per year (typical French PhD stipend range)

Benefits

Doctoral Research Funding through ADI Program
Academic Research Environment
International Collaboration Opportunities
Access to Advanced Research Facilities

Key Responsibilities

Develop innovative digital twin solutions for additive manufacturing processes
Implement machine learning algorithms for process optimization
Conduct research on sustainable manufacturing practices
Publish findings in academic journals
Present research at international conferences
Collaborate with industry partners

Requirements

Education

Master's degree in Mechanical Engineering, Manufacturing Engineering, Industrial Engineering, or Automation and Control

Required Skills

Excellent analytical skills Strong interest in manufacturing processes Expertise in modeling and simulation Strong communication skills Proficiency in English Computational modeling skills
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Sauge AI Market Intelligence

Industry Trends

Digital twin technology is experiencing rapid adoption in manufacturing, with a projected market growth of over 35% annually through 2027 Sustainable manufacturing practices are becoming mandatory due to regulatory pressures and market demands Integration of AI and ML in manufacturing processes is creating new research opportunities and career paths Additive manufacturing is disrupting traditional production methods, particularly in aerospace and medical sectors

Role Significance

Typically part of a research group of 5-10 members, including other PhD students, postdocs, and supervising professors
Doctoral Researcher position with significant autonomy in research direction and methodology

Key Projects

Development of digital twin frameworks for additive manufacturing Implementation of machine learning algorithms for process optimization Sustainability analysis and optimization of manufacturing processes Industrial case studies and validation of research findings

Success Factors

Strong foundation in both theoretical and practical aspects of manufacturing processes Ability to bridge the gap between computational modeling and physical manufacturing systems Excellence in scientific writing and presentation Capacity to work independently while contributing to team objectives

Market Demand

High demand for specialists in digital twin technology and sustainable manufacturing, with particular growth in European research institutions and industrial R&D centers

Important Skills

Critical Skills

Digital twin modeling expertise due to its central role in the research focus Machine learning knowledge for process optimization Sustainable manufacturing principles understanding Strong programming and simulation capabilities

Beneficial Skills

Knowledge of specific AM technologies Experience with industrial automation systems Familiarity with sustainability assessment methods Project management skills

Unique Aspects

Integration of sustainability focus with cutting-edge digital manufacturing technologies
Strong emphasis on practical industrial applications
International research environment with potential for global impact
Access to advanced manufacturing and computational facilities

Career Growth

3-year PhD program with potential for postdoctoral extension

Potential Next Roles

Postdoctoral Researcher R&D Engineer in Industry Manufacturing Technology Consultant Academic Research Position

Company Overview

Université Paris-Saclay GS Sciences de l'ingénierie et des systèmes

Université Paris-Saclay is one of France's leading research universities, particularly strong in engineering and physical sciences

Ranked among top European institutions for engineering research with strong industry connections
Major research hub in the Paris region with significant international collaborations
Academic research environment with emphasis on innovation and international collaboration
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