Vacant job

About the Research Project

The recruitment of a PhD student in textile management at the Department of Business Administration and Textile Management is conducted within the research profile TexAI – Knowledge in AI-driven innovation for circular ecosystems in the textile and fashion industry, funded by the KK Foundation. TexAI is an interdisciplinary research and innovation initiative carried out in collaboration between the research areas of Textile & Fashion and Business & IT. The overarching goal is to implement AI-driven solutions within textile technology, fashion and textile design, textile management, and business-oriented IT, to promote a green transition in the textile and fashion industry. The project is conducted in close collaboration between academia, industry, and business.

The PhD student will be affiliated with the research groups Textile Systems and Computational Intelligence (TSCI) and Textile Value Chain Management (TVCM).

TSCI uses a systems-oriented perspective and advanced computational intelligence in studies of the complex interactions between production units, technologies, and organizational structures within manufacturing and supply chain environments, aiming to enhance system performance, agility, and sustainability. TVCM focuses on the development of industry structures and business models within textile and fashion companies that enable sustainable value creation for the industry, the environment, and society at large.

Your Responsibilities

The position as a PhD student encompasses four years of full-time studies to achieve a doctoral degree. The research studies are part of the project “TexAI – Knowledge in AI-Driven Innovation for Circular Ecosystems in the Textiles and Fashion Industry,” which is funded by the KK Foundation.

As a PhD student, you will conduct your own education and research up to a maximum of 100% full-time. During your research studies, you are expected to:

• Conduct research on data requirements, data standardization, and data integration in textile value chains to enable AI-based applications, using methods and models from operations analysis, data analysis, or artificial intelligence/machine learning.
• Identify, structure, and prioritize relevant data criteria and data sources along the textile value chain using multi-criteria decision analysis.
• Develop frameworks and models for standardized data collection, data sharing, and data usage among actors in textile supply chains.
• Analyze how data from the value chain can support decision-making related to sustainability and circularity, as well as how such data-driven decisions can be implemented in industrial practice.
• Develop and apply a scientific conceptual framework based on industrial economics, supply chain management, and data-driven decision-making.
• Establish and maintain collaboration with relevant stakeholders in the global textile and fashion industry for data collection and validation.
• Collect, process, and analyze empirical data using appropriate analytical tools and identify both scientific and practical contributions.
• Write and publish scientific articles and reports and present research results at scientific conferences and to representatives from the textile and fashion industry.
• Use relevant and creative tools to tailor research communication to different target groups within the project.

Your Qualifications

Employment is subject to meeting the requirements and being admitted as a doctoral student. Admission is to the doctoral program in textile management.

Basic eligibility for doctoral education at the doctoral level is granted to those who have completed a degree at the advanced level, or have completed course requirements of at least 240 higher education credits, of which at least 60 higher education credits at the advanced level, or otherwise acquired equivalent knowledge within or outside the country. Special eligibility is granted to those who have completed a degree at the advanced level in textile management, industrial economics, or equivalent competence assessed as equivalent.

Other Requirements:

• Good knowledge and experience in collecting, structuring, and analyzing data from supply chains.
• Good knowledge of the textile industry, textile supply chains, actors' goals, and decision-making within supply chains.
• Good collaboration and initiative skills.
• Ability to work in a structured manner and prioritize tasks.
• Good knowledge of English, both spoken and written.

Meritorious:

• Participation in research on supply chain management, preferably within textiles and fashion or similar industrial sectors.
• Basic knowledge of textile materials and processes.
• Basic knowledge of certain software programming.
• Practical experience working in the textile or clothing industry with sustainability, production, and automation-related issues.
• Experience producing scientific publications.
• Experience in research communication.

Admission and Employment

Once you have been admitted to the doctoral program, you will be assigned a PhD position. The initial PhD position can last for a maximum of one year and can then be extended for up to two years at a time. The total time as a PhD employee may not exceed a maximum of eight years, but must not exceed the time corresponding to four years of full-time doctoral studies. Salary is determined according to the university's local agreement for doctoral students.

Employment form: The position as a PhD student encompasses full-time for four years in accordance with the Higher Education Ordinance Chapter 5.
Employment rate: 100 %
Start date: May 1, 2026, or by agreement.
Location: Borås.
Diary number: PA2026/21

How to Apply

Welcome with your complete application including the diary number and attachments via our recruitment system no later than March 23.

How to apply – create an account and see which documents you need to attach

The University of Borås encourages applications from individuals with diverse backgrounds, experiences, and perspectives as this enriches and strengthens our operations. We strive for a balanced gender distribution among employees and value diversity in our operations.

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