PhD: Circular Silicon Powders for Additive Manufacturing

Utwentecareers; Enschede; On-site; EUR 38,000 - 49,000; Full time

Benefits offered by this job

Holiday allowance

Year-end bonus

Pension scheme

Job summary

University of Twente offers a fully funded four-year PhD position focused on developing circular production routes for silicon and silicon carbide, blending materials science, powder technology, additive manufacturing, and data science.

You will develop mechanochemical routes to convert silicon-rich waste into high-purity powders, establish AM process windows, and contribute to machine-learning models linking powder properties to final parts. The project spans academia and industry.

Qualifications

• Master's degree in a relevant engineering/science discipline.
• Strong interest in powder technology, ceramics, and additive manufacturing.
• Experience with materials characterisation techniques and mechanical/thermal testing is desirable.
• Excellent communication skills in English, both spoken and written.

Responsibilities

• Develop and optimise routes for converting silicon-rich waste into high-purity powders.
• Establish additive manufacturing process windows for circular powders and investigate melt behaviour.
• Contribute to data-driven machine-learning models linking powder characteristics with final component properties.
• Publish research findings in high-impact venues and collaborate with international partners.

Skills

Powder technology

Ceramics

Additive manufacturing

English proficiency

Analytical skills

Independent & collaborative work

Education

Master's degree in Materials Science and Engineering

Master's degree in Mechanical Engineering

Job description

University of Twente offers a fully funded four-year PhD position focused on developing circular production routes for silicon and silicon carbide, blending materials science, powder technology, additive manufacturing, and data science.

You will develop mechanochemical routes to convert silicon-rich waste into high-purity powders, establish AM process windows, and contribute to machine-learning models linking powder properties to final parts. The project spans academia and industry.