PhD Student in Electrospinning Process Control: Tallinn University of Technology is inviting applications for a PhD position focused on developing an AI model for controlling the multilayer electrospinning process. The research aims to enhance quality control in the production of nanofibrous membranes using innovative modeling and machine learning techniques.
PhD Student in Developing Edge AI Model for Multilayer Electrospinning Process Control
Designation:
PhD Candidate
Table
| Detail | Information |
|---|---|
| Organisation/Company | Tallinn University of Technology |
| Department | Thomas Johann Seebeck Department of Electronics |
| Job Type | Full-Time |
| Offer Starting Date | 1 Dec 2025 |
| Contract Type | Temporary |
| Hours Per Week | 40 |
| Application Deadline | 24 Oct 2025 – 23:59 (Europe/Tallinn) |
| Location | Tallinn, Estonia |
| Research Area | Biomedical Engineering, Machine Learning, Polymer Technology |
Eligibility/Qualification:
- Education Level: Master Degree or equivalent in Biomedical Engineering, Polymer Technology, Applied Physics, Chemical Engineering, or related fields.
- Skills/Qualifications:
- Excellent command of English.
- Experience in analytical methods (e.g., profilometry, SEM), machine learning, modeling, or embedded software.
- A passion for working in a dynamic, interdisciplinary, and international team.
Description:
This PhD position will focus on constructing a scalable, multiparameter model mapping observable process parameters (e.g., spinneret voltage, flow rate) to nanofiber product quality (e.g., morphology, diameter variance). The project aims to develop technologies for inline quality control of electrospun nanofiber meshes, facilitated by machine learning applications that can run efficiently on embedded hardware.
How to Apply:
Interested candidates can apply through the following link: Apply Here
Last Date to Apply:
24 October 2025 – 23:59 (Europe/Tallinn)
This scholarship presents a valuable opportunity for candidates interested in cutting-edge research in nanotechnology and machine learning within an innovative environment.
