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University of Twente in the Netherlands offers a fully funded PhD position to convert silicon-rich waste into high-quality silicon and silicon-carbide feedstocks for additive manufacturing. The role combines mechanochemical processing, powder/ceramic characterisation, additive manufacturing, mechanical and thermal testing, and data-driven structure–property modelling.
Applicants should hold an MSc in materials science and engineering or a closely related field, with experience in powder
University of Twente · Faculty of Engineering Technology
Netherlands
Apply by 22 Oct 2026
About This Opportunity
University of Twente is recruiting a fully funded PhD candidate to convert silicon-rich waste into high-quality silicon and silicon-carbide feedstocks for additive manufacturing. Applications close on 22 October 2026 at 23:59 Europe/Amsterdam.
The project develops circular powder-processing routes from silicon-rich waste to printable silicon and silicon-carbide materials. It combines mechanochemical processing, powder and ceramic characterisation, additive manufacturing, mechanical and thermal testing, and data-driven process–structure–property modelling.
silicon carbide circular materials additive manufacturing powder processing materials recycling
Level PhD
Attendance On-campus
Time Commitment Full-time
Duration 4 years
Field Circular Silicon and Silicon Carbide Additive Manufacturing
Specialization Materials Science & Engineering
Organization Type University
Funding & Compensation
Amount EUR 3204-4051 / Per Month
Funding Type Salary
Funding Status Fully Funded
Period Per Month
Applicants should hold an MSc in materials science and engineering, mechanical engineering or another closely relevant field.
Knowledge or interest in powder processing, ceramics and additive manufacturing is expected. Materials characterisation, mechanical or thermal testing, powder metallurgy or mechanochemistry experience is advantageous.
Powder processing Silicon carbide Additive manufacturing Ceramic processing Mechanochemistry Materials characterization Machine learning