PhD Fellowship position in Quantum-Inspired Evolutionary Algorithm for Multi-Objective Integrat

PhD Fellowship position in Quantum-Inspired Evolutionary Algorithm for Multi-Objective Integrative Optimization

OsloMet's vision is to deliver knowledge to solve societal challenges. Norway's most urban and 3rd largest university.

The Department of Computer Science at the Faculty of Technology, Art and Design (TKD) has a vacant PhD Fellowship position in the field of artificial intelligence with quantum computing. The project combines evolutionary algorithms (EAs) with quantum computing, focusing on its application in complex multi-objective optimization problems. The PhD candidate will be part of the Artificial Intelligence academic group.

Area of research

The primary objective of this project is to formulate and implement a multi-objective quantum-inspired EAs (QEA) tailored specifically for classical computers, with a focus on addressing the prevalent challenges in the domain of multi-objective integrative optimization (MIO) problems. Real-world optimization problems, prevalent in industries, are often complex, involving different interrelated optimization problems with multiple interconnected and conflicting objectives. Most of these involved independent optimization problems are interrelated, and combining them into a global integrative optimization problem is therefore necessary. This proposal considers formulating an MIO problem by combining k optimization problems, resulting in k objective functions. As a result, instead of seeking a single solution, the approach is to provide a set of alternatives (Pareto-optimal front) that reflect the trade-off between the objectives resulting from the MIO, allowing decision-makers to choose based on their preferences.

The MIO problems inherently fall within the NP-hard class, and traditional optimization methods often cannot handle the complexity of such real-world MIO problems, resulting in suboptimal solutions and long computational times. Evolutionary Algorithms (EAs), inspired by natural selection processes, have demonstrated effectiveness in handling NP-hard problems due to their stochastic nature, population-based exploration, and global search capabilities. However, issues like premature/slow convergence and imbalanced exploration-exploitation trade-offs limit their performance.

Recently, the emergence of quantum-inspired EAs (QEAs) has opened up new avenues for enhancing the effectiveness of EAs by striking a better balance between exploration and exploitation. Drawing inspiration from quantum mechanics, QEAs integrate concepts such as superposition, quantum parallelism, entanglement, interference, coherence, and measurement into the existing EA framework. Recent advancements have underscored the significant advantages of QEAs over classical EAs, demonstrating success in solving complex NP-hard problems that were previously deemed computationally intractable for classical computers. However, existing QEAs are typically designed for single optimization problems and exhibit optimal efficiency on specialized quantum hardware rather than classical computers. They also encounter challenges in maintaining coherence and leveraging entanglement for efficient exploration, necessitating further exploration of quantum operators and encoding schemes that can adapt to diverse problem structures and objective functions.

This project aims to bridge this gap by developing a novel multi-objective QEA that is specifically designed for classical computing environments. By utilizing quantum-inspired techniques, the objective is to provide industries with a practical and efficient solution for tackling real-world complex multi-objective optimization challenges in areas such as manufacturing and logistics. This research goal encompasses both theoretical and practical dimensions, focusing on contributing significantly to developing multi-objective QEA.

The position is advertised as a 3-year position with 100% research, or a 4-year position with 75% research and 25% other career-advancing work. The goal must be to complete the PhD program/degree within the decided time frame. The decision on a 3- or 4-year position will be discussed as part of the interviews in the hiring process.

Qualification requirements and conditions

• Master's degree in Computer Science, Artificial Intelligence, Quantum Computing, Applied Mathematics or a related field. The degree must contain 120 credits (ECTS). All exams for the master's degree must be completed by 30 June 2025.
• An academic profile that suits the research group's and the department's needs.
• Good communication skills in English, oral and written.

The following grade requirements are a condition for employment in the position:

• Minimum average grade B on subjects included in the master's degree.

In assessing the applicants, emphasis will be placed on the department's overall needs and the applicant's potential for research within the field.

Preferred selection criteria

• Highly motivated and goal-oriented, with a passion for research in evolutionary quantum algorithms.
• Strong analytical skills to tackle complex problems in the field.
• Good abilities in problem-solving and understanding new theories.
• Ability to work both independently and in a team setting.
• Ability to conduct and report research in a structured and professional manner.
• Personal suitability will be emphasised.

We can offer you

• An exciting job opportunity at Norway's third largest and most urban university
• Opportunities for professional development
• Beneficial pension arrangements with the Norwegian Public Service Pension Fund (spk.no/en/)
• Beneficial welfare schemes and a wide range of sports and cultural offers
• Free Norwegian language classes to employees and their partners/spouses
• Workplace in downtown Oslo with multiple cultural offers

Application process

To be considered for the position, you must upload the following documents by the application deadline:

• Application letter describing your motivation and how your professional profile is relevant for this position.
• Copies of diplomas and transcripts for bachelor's and master's degrees (which list subjects and grades) and certificates. Please note that a description of the grading system at the university/country where you took your degree must be attached. This must be an official document from your university. Foreign diplomas must be translated into English by the university that issued the diploma.
• Name and contact information of two references (name, relationship, e-mail and telephone number).
• Scientific work that you want to be assessed.
• Applicants from countries where English is not a first language must submit the result of an official language test. The following applicants are exempt from this language requirement:
  • Applicants from EU/EEA countries.
  • Applicants who have completed at least one year of study in Australia, Canada, Ireland, New Zealand, Great Britain or the United States.
  • Applicants who hold an "International Baccalaureate (IB)" diploma.

Official diploma and transcript must be submitted before taking up the position, no later than 01.10.2025. If your educational institution is not able to deliver an official diploma by the deadline, you must submit documentation from the institution that confirms that your master´s degree is completed by the same deadline.

We only process applications sent via our electronic recruitment system and all documents must be uploaded within the deadlines stated in the announcement text for your application to be processed. The documents must be in English or a Scandinavian language. Translations must be authorized. You must present originals at any interview. OsloMet checks documents, so that you as a candidate will get a real evaluation and fair competition.

Other information

If you would like more information about the position, feel free to contact:

• Head of the Group: Boning Feng, email: boning.feng@oslomet.no

The position is paid according to the pay scale for Norwegian state employees, position code 1017 PhD fellow, NOK 532 200 per year.

If you would like to apply for the position, you must do so electronically through our recruitment system.

Deadline for applications: 01.05.2025
Ref.: 25/03238

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