Norwegian version

Ph.D. fellowship position in Quantum Computing in the context of Artificial Intelligence

OsloMet – Oslo Metropolitan University is one of Norway’s largest universities, with more than 20,000 students and 2,000 employees. OsloMet delivers knowledge to solve societal challenges, in close cooperation with the society and employers. OsloMet is an urban and diverse university with a clear international profile, and an attractive place to work and study with campuses in Oslo city center and at Kjeller in the Municipality of Lillestrøm. Our location in the metropolitan area gives us good opportunities to understand and benefit from the city’s diverse population.

The Faculty of Technology, Art and Design (TKD) offers higher education and research and development (R&D) activities within technical subjects, arts and design. The Faculty has approximately 3.000 students and 280 staff members and is situated at Pilestredet Campus in downtown Oslo and at Kjeller Campus in Viken.

The Department of Computer Science offers three bachelor’s degree programmes, a master’s degree programme, and is part of a cross-departmental PhD program. Academic staff at the department are pursuing research in a wide range of areas including computer science, the natural sciences, and innovation and management. Both students and researchers are also involved in an increasing number of interdisciplinary initiatives across the university.

The Department of Computer Science offers three bachelor’s degree programmes and a master’s degree programme, in addition to research and development activities. The department has approximately 1500 students and consists of 60 staff members.

Job Description

The candidate will be affiliated to the Department of Computer Science and to the OsloMet/SimulaMet AI Lab, working within the Excellent Academic Environment, NordSTAR Nordic Center for Sustainable and Trustworthy Artificial Intelligence Research, which is hosted by OsloMet and includes partners from several research centres and universities in Norway, such as Simula Research Laboratory and the Norwegian University of Science and Technology (NTNU).

During the last few years, Quantum Computing (QC) has evolved from a theoretical academic subject into a fast growing part of the IT industry, with many practical real-life applications. Researchers in different fields started to explore the possibility of using QC ideas to boost the performance of existing Artificial Intelligence (AI) solutions or to create new, fully quantum, ones. Many ideas and suggestions have already been made, but one missing issue is a performance analysis of different quantum AI schemes, quantification of their reliability, and estimation of their potential by testing them with a set of practical problems conventionally used in the AI field. The project will explore these issues.

Modern AI can document impressive results and even outperform human beings on many tasks. The methods are however also associated with many challenges that limit the trust when the methods are applied. NordSTAR aims to improve trustworthiness in AI methods by addressing four challenges. 1) It is usually hard to understand the mechanism in complex AI methods leading to the predictions, 2) there are important human factors in the application of AI, both legally and ethically, 3)  the methods usually are not able to quantify how certain they are about their decisions, 4) running large and complex machine learning systems opens up security issues.

NordSTAR is composed by 5 main research areas:

The candidate’s project is part of NordSTAR’s aims and challenges, focusing on Quantum AI.

The project assumes interdisciplinary collaboration within NordSTAR groups  as well as with the NordSTAR research partners at OsloMet, including the research groups Mathematical Modelling and Applied Artificial Intelligence, other Norwegian partners such as the Simula Research Lab, and colleagues from research institutions in Germany and South Korea.

Project overview

Current research activity at the interface of QC and AI can be divided into two categories, depending on what issue is addressed: (1) How QC can profit from AI technologies? (2) Can quantization of AI solutions improve their efficiency?

Within this project we will address both these facets:

1) Optimization of Digital Quantum Simulation with AI methods: Digital Quantum Simulations (DQSs) is the emulation of complex systems or processes by transforming them into quantum models. The latter can subsequently be implemented on quantum computers. Empowered with Quantum Computing Supremacy, the DQS idea promises to solve complex problems which may be considered as intractable for classical computers. An important step in the realization of DQS is the transformation of the quantum model into a quantum circuit, which, in turn, is implemented on a quantum computer. This step is called ‘quantum compiling’ and constitutes a complex optimization problem: A given quantum model should be approximated by a quantum circuit in the best way, i.e., with a minimal error, under given constrains such as the set of available quantum gates, maximal total number of gates, etc. It was suggested very recently that Machine Learning and AI solutions can be used to develop a new, highly efficient generation of quantum compilers. The aim of our project is to uncover the full potential of the AI-based compiling approach by testing different Machine Learning (ML) schemes and AI algorithms with realistic DQS problems as testbeds. The objective of this part is to obtain a collection of results convincingly demonstrating - or refuting - efficiency of the AI-based approach to quantum compiling.

2) Exploration of practical potential and efficiency of quantum AI schemes: The objective of this part of the project is not to design a new quantum AI solution, but rather to carefully evaluate the spectrum of the existing ones. The main problem of the current Quantum AI field is the theory-practice gap: Most of the proposed schemes are illustrated by using toy models/problems so that the corresponding results do not provide evidence of a quantum advantage. Could quantum effects  provide exponential advantages for practical learning problems? (there is a growing  scepticism among some experts as  to this point) Could they provide any substantial advantage? To answer these questions, we will explore the potential of existing Q-AI schemes by testing them with a set of practical problems conventionally used in the AI field.

For further details about the project contact the supervisors:

 

The Ph.D. fellowship will be offered for a period of three years (100% position), or alternatively, four years including 25% compulsory work (teaching and supervision activities or research administrative work). The decision on whether a 3 or 4-year position is suitable will be discussed as part of the interview process. The successful applicant should have the goal to complete the Ph.D. program within this time frame and receive a Ph.D.

The position will be available from 01.08.2021. 

Qualification requirements

We are looking for enthusiastic candidates that must:

The Ph.D. candidate will be admitted at the faculty’s Ph.D. program in Engineering Science.

Candidates who already hold a PhD in the same or similar field may not apply.

The successful candidate will be expected to participate actively in the NordSTAR research.

It would be an advantage if the candidate

Ability to and interest for public dissemination of the research will also be considered an advantage.

Desired personal skills:

It is important for OsloMet to reflect the population of our region, and all qualified candidates are welcome to apply. We make active efforts to further develop as an inclusive workplace and to adapt the workplace if required.

Application

In order to be considered for the position you must upload the following documents together with your online application by the final date for the application:

Applicants from countries where English is not the first language must present an official language test report. The following applicants are exempt from the abovementioned language requirements:

The following tests qualify as such documentation: TOEFL, IELTS, Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English (CPE). Minimum scores are:

All the documents must be written in English or a Scandinavian language. Documents (for instance diplomas and transcript of grades) issued in a different language than English or one of the Nordic languages, must be translated into one of the Nordic languages or English. Translations must be conducted by a government authorized translator. Originals must be presented if you are invited for an interview. OsloMet performs document inspections in order to give you as a candidate a proper evaluation and ensure fair competition.

Please note that incomplete applications will not be considered.

We can offer you

For information on living and working in Oslo please read here.

For further details about the position contact the directors of NordSTAR:

If you have technical questions about uploading the application, please contact the HR Department, HRTKD@oslomet.no

 

Remuneration

Salary is set in accordance with the Norwegian State Salary Scale, position code 1017 PhD Fellowship pay grade 54, NOK 482 200. By law 2% of the salary is deducted as to the Norwegian State Pension Fund.

The position adheres to the Norwegian Government’s policy that the national labour force should to the greatest possible extent reflect the diversity of the population. Therefore, we encourage qualified candidates with immigrant background or reduced functional ability to apply for this position. OsloMet is an IA (Inclusive Workplace) enterprise and operates in compliance with the Norwegian IA agreement.

According to the Norwegian Freedom of Information Act (Offentleglova) your name may be published on the public applicant list even if you have requested non-disclosure. You will in this case be contacted before your name is published.

Deadline for applications: 15.03.2021

 

OsloMet is a Charter & Code certified institution by the EU Commisson holding the right to use the logo HR Excellence in Research (HRS4R). OsloMet is a member of the EURAXESS network supporting a positive work environment for researchers in motion. OsloMet has signed The Declaration on Research Assessment (DORA). DORA recognizes the need to improve the ways in which the outputs of scholarly research are evaluated.