Postdoctoral Research Fellow in Data-Driven Optimization for Reverse Logistics

The applicants will be assessed by an expert committee. The committee's mandate is to undertake an assessment of the applicants' qualifications based on the written material presented by the applicants, and the detailed description draw up for the position. A copy of the assessment report will be sent to all applicants.

The applicants who are assessed as best qualified will be called to an interview. The interview should among other things, aim to clarify the applicant's motivation and personal suitability for the position. A trial lecture may also be held.

The Postdoctoral Research Fellow is expected to contribute to the following tasks:

• Develop data-driven qualitative/quantitative methods to identify and analyze key factors influencing reverse logistics system planning, including consumer return behavior and electric truck adoption.
• Design and implement a two-stage proactive-reactive optimization framework that integrates exact/metaheuristic methods (proactive stage) with reinforcement learning approaches (reactive stage) for dynamic routing and real-time remanufacturing planning to minimize empty trips, carbon emissions, and idle capacities in reverse logistics systems.
• Test and validate the models and solution methods with real-world case studies.
• Publish research findings in leading scientific journals and present results at major conferences.
• If desired, contribute to the supervision of PhD candidates and Master's students.
• If desired, support teaching activities within the Master's Programme of Industrial Engineering, including lectures, seminars, and the development of new educational materials.
• Contribute to proposal writing and reporting activities for national and international funding agencies.

For further information about the position, please contact Professor Hao Yu:

• email: [email protected]

or Head of Department Prof. Wei Deng Solvang:

• email: [email protected]

This position requires a Norwegian doctoral degree (PhD), or an equivalent foreign doctoral degree in Industrial Engineering, Logistics Engineering, Transportation Engineering, or a closely related field.

The position also requires:

• Demonstrated experience in applying a combination of data-driven qualitative and quantitative methodologies within logistics systems is required. Relevant methods include, but are not limited to, survey design, data envelopment analysis, machine learning, mathematical optimization, simulation, and case studies. Experience in applying these methods to address environmental and social sustainability challenges in logistics systems will be considered an advantage.
• Experience with competitive research funding applications (e.g., national or international grants) will be considered an advantage.
• Excellent oral and written communication skills in English are required.
• Applicants must submit a 3-5 page research plan aligned with the scope of the position, clearly outlining objectives, research scope, and proposed methodologies, with strong emphasis on relevance, rigor, and potential impact.

In the assessment the main emphasis will be attached to the submitted works and the research plan for the qualifying work. Emphasis shall also be attached to experience from popularization/dissemination and academic policy and administrative activity.

During the assessment emphasis will be put on the candidate's motivation, potential for research, and personal suitability for the position. We are looking for candidates who:

• Have good collaboration skills
• Have good communication and interaction with colleagues and students
• Wants to contribute to a good working environment

At UiT we put emphasis on the quality, relevance and significance of the research work and not on where the work is published, in accordance with the principles of The San Francisco Declaration on Research Assessment (DORA).

UiT The Arctic University i Norway is working actively to promote equality, gender balance and diversity among employees and students, and to create an inclusive and safe working environment. We believe that inclusion and diversity is a strength, and we want employees with different competencies, professional experience, life experience and perspectives.