Ph.D. fellowship position in Quantum Computing and Quantum AI in the context of Complex Optimization Problems - Hiring in process/Finished, not possible to apply

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.

Job Description

The candidate will be affiliated to the Department of Computer Science and to the OsloMet/SimulaMet AI Lab, working within the Center of Research Excellence, 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).

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:

• Understandable and explainable models (led by Hugo Hammer)
• Human Factors in AI (led by Elena Parmiggiani)
• Security, safety and reliability (led by Ahmed Elmokashfi)
• Biologically inspired computational systems (led by Stefano Nichele)
• Quantum AI (led by Sergiy Denysov)

The candidate’s project is part of NordSTAR’s aims and challenges, focusing on application
of QC technologies for solution of complex optimization problems.

Quantum computing already demonstrated potential to solve complex optimization
problems much faster than the largest present-day computational clusters. At the same
time, we are now facing some hard optimization problems which were not designed as
proof-of-concept abstractions but arose from practical needs. Biological process-based mathematical models, when initialised and calibrated with patient-specific data, may
dramatically enhance the efficiency of current cancer therapies. However, even models
with a moderate level of description give rise to optimization problems of such a
complexity that their exact solutions cannot be obtained. Approximated algorithms such
as simulated annealing have been used, but they often require too long computation time
even when the corresponding algorithms are implemented by using high-performance
computing technologies. At the same time, it is evident that such an approach would be
relevant to the clinical setting only if the time needed to solve the optimization problem
is compatible with the timescale of clinical decision making. Currently, the gap between
these timescales is substantial. In this scope, although still in their infancy currently,
Quantum Computing and Quantum AI constitute a promising perspective.

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, the Cancer Modelling Group
at the Institute of Basic Medical Sciences of the University of Oslo, and other Norwegian
partners such as the Simula Research Lab.

For further details about the project contact:

• Asso.Prof. Sergiy Denysov (main supervisor), sergiyde@oslomet.no
• Prof. Pedro Lind, pedro.lind@oslomet.no
• Asso. Prof. Harek Haugerud, haugerud@oslomet.no
• Researcher Alvaro Köhn-Luque (UiO), a.k.luque@medisin.uio.no

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 01.04.2022.

Qualification requirements

We are looking for enthusiastic candidates with a master’s degree in computer science,
physics, or mathematics.

The candidate must

• have completed a master’s degree (equivalent to 120 credits) with a grade B or
  better and a bachelor’s degree with grade C or better and with a background in
  an area relevant for the PhD project.
• be proficient in written and spoken English
• be familiar with scientific computing and programming in Python, C++, MATLAB
  or similar.

The Ph.D must have approved admission to the Ph.D. program in Engineering Science within 3
months after employment in the position.

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

• is familiar with the basics of machine learning (ML), optimization and AI theory
• is familiar with the basics of quantum computing or quantum physics
• have an ability to work interdisciplinary

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

It is important to OsloMet to reflect the population of our region, and all qualified candidates are welcome to apply. We make active efforts to further develop OsloMet as an inclusive workplace and to adapt the workplace if required. You are also welcome to apply for a position with us if you have experienced periods where you have not been in work, education or training.

Expert evaluation

Applicants will be assessed by an expert committee. All applications will be reviewed by
the faculty before the application papers are forwarded to the expert committee. Applications from applicants who are not qualified, will not be forwarded for assessment.

When assessing and ranking the qualified applicants, emphasis will be placed on

• past scientific work (where relevant).
• experience from previous research activities and participation in research
  projects.
• experience from teaching and/or supervision at the college or university level.

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 applications:

• Cover letter, stating your reasons for applying and provide reasons for why you are particularly qualified for this Ph.D. fellowship position.
• CV and all pages of certificates/diplomas. The certificates/diplomas must include ECTS grades (A– F). Foreign diplomas must be translated into English by the degree-conferring institution. Education taken abroad should preferably be recognised in advance by NOKUT, and a confirmed copy of the letter of recognition should be enclosed.
• Master's degree/second degree thesis (and any other scholarly work that the expert committee should take into consideration). 
  must be submitted prior to signing of the contract.
• List of publications, if relevant.
• The names and contact details of at least two references.
• 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:
  • Applicants from EU/EEA countries.
  • Applicants who have completed one year of university studies in Australia, Canada, Ireland, New Zealand, the UK or USA.
  • Applicants with an International Baccalaureate (IB) diploma.
• 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:
  • TOEFL: 600 (paper-based test), 92 (Internet-based test).
  • IELTS: 6.5, with no section lower than 5.5 (only Academic IELTS test is accepted).

All the documents must be written in English. Any translations must be authorized by the government. 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

• An exciting job opportunity at Norway’s third largest and most urban university.
• Opportunity to be a part of a dynamic professional environment and unique academic network.
• Beneficial pension arrangements with the Norwegian State Pension Fund.
• Good employee welfare arrangements.
• Working location in downtown Oslo with multiple cultural offers.

Practical information about relocation to Oslo and living in Norway.

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

• Prof. Anis Yazidi, anis.yazidi@oslomet.no
• Prof. Pedro Lind, pedro.lind@oslomet.no

Remuneration

Salary is set in accordance with the Norwegian State Salary Scale, position code 1017 PhD Fellowship, pay grade 54, NOK 491 200. 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.

Please apply electronically to this position by clicking on the button «Click here and apply».

Ref:  21/04465

Deadline for applications: January 21 2022