PhD Candidate in Environmental Engineering (Waste Management, Remote)

Job summary

This fully funded PhD position focuses on developing advanced waste management models and frameworks, particularly enhancing the EASETECH models for life cycle analyses and waste prevention strategies. The research aims to create more dynamic and flexible approaches to quantify the environmental impacts of waste management systems.

Qualifications

• A two-year master's degree (120 ECTS points) in Environmental Engineering or a related field.
• Extensive experience in life cycle assessment (LCA) modeling, specifically with EASETECH and Brightway2.5.
• Professional experience in developing Python-based waste models.
• Strong background in environmental engineering with a specialization in residual resource engineering.
• Proficiency in statistics and Markovian models for framework development.
• Excellent communication skills in English, capable of conveying complex scientific topics to diverse audiences.

Responsibilities

• Develop a Python-based digital EASETECH twin to improve assessment methods for waste management systems.
• Analyze the effects of time and geography on waste management through innovative modeling techniques.
• Create methods for estimating accumulated stocks of substances in waste management systems.
• Collaborate with industry partners and researchers to develop open-source LCA approaches.

Skills

• Proficiency in Python programming and experience with EASETECH and Brightway2.5.
• Strong analytical and problem-solving skills.
• Ability to work independently and collaboratively in a research environment.
• Creativity and curiosity in research pursuits.

Education

• Required: Two-year master's degree in Environmental Engineering or a similar academic level.

Tools

• Python, EASETECH, Brightway2.5, statistical analysis software.