Ph.D. fellowship position in Artificial Intelligence in the field of musculoskeletal health and big data - Hiring in process/Finished, not possible to apply

The Center of Intelligent Musculoskeletal Health (CIM) was recently approved as one of five excellent academic environments at OsloMet. The CIM will represent a research arena for researchers across disciplines at OsloMet and beyond, including health scientists from The Musculoskeletal Health research group (Faculty of Health Sciences) and computer scientists at the OsloMet AI Lab (Faculty of TKD), national and international research partners, and user representatives for patients, health care services and higher education.

Job Description

The Ph.D. Fellow will be employed at the Department of Computer Science and affiliated with the Center of Intelligent Musculoskeletal Health (CIM). Researchers affiliated with the CIM conduct high-quality research on musculoskeletal health in a life course perspective, embracing adolescents, young adults, adults, and older people.

Among the central goals of the CIM is to bring together experts in technological science, health science and social science to conduct epidemiological and intervention research using advanced methods in analyses of large datasets, including Artificial Intelligence (AI) and Machine Learning (ML) technologies.

The CIM research activities intersect three core areas:

• Epidemiology and Clinical Research
• Intelligent Health and Innovation
• Implementation and Dissemination

Research activities at the CIM will aim to enhance prognostic modelling of musculoskeletal health outcomes. The scientists at CIM will also investigate underlying mechanisms for musculoskeletal disorders. The future goal is to develop, evaluate, and implement effective innovative and practical solutions to improve the musculoskeletal health in the population.

The Ph.D.-fellow will be expected to collaborate with researchers at the CIM-centre, other research partners at OsloMet, and national and international partners.

The Ph.D.-fellow will be admitted at The Faculty of TKD’s Ph.D. program in Engineering Science.  Candidates who already hold a PhD in the same or similar field may not apply. The Ph.D. fellow must complete courses in the Ph.D program in Health Science amounting to a total of minimum 10 ECTS.

The Ph.D.-fellowship will be offered for a period of three years (100% position). The successful applicant should complete the Ph.D. program within this time frame and receive a Ph.D.

Project overview

Musculoskeletal disorders are the second most common cause of disability worldwide, and with the ageing population they represent one of the largest future challenges for health and welfare at the societal level. Electronic health registries are propelling the development of large pools of health data. Norway has multiple nationwide health registries with possibility for linkage between registries, and integration of data from different registries can pave the way for powerful research.  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 in specific contexts.

The scope of this Ph.D.-project lies in the field of machine learning (ML) algorithm development and registry-based epidemiology related to musculoskeletal health in adolescence and emerging adulthood. An overall goal of this Ph.D.-project is to improve the trustworthiness in AI by contributing knowledge about the mechanism in complex AI and ML methods in the field of musculoskeletal health, leading to enhanced predictions of  health/disease outcomes, and methods to quantify the certainty of ML-based clinical decision-tools. The Ph.D. project will utilise AI and ML to predict musculoskeletal health outcomes, and outcomes relevant for educational and work participation. The project will use detailed large-scale linked data from multiple sources, including a national student survey, regional cohort studies, and national administrative and health registries. The research protocol will be made available for candidates who are invited for an interview. The Ph.D.-project is expected to encompass scientific investigations reported in at least three scientific papers of which the Ph.D. fellow is the first author.

For further details required in the proposal, please contact the supervisor Associate Prof. Hårek Haugerud, haugerud@oslomet.no or Prof. Anis Yazidi, anisy@oslomet.no.

The starting date should be no later than three months after the candidate receives an offer of the position.

Qualification requirements

We are looking for candidates that must:

• have completed a Master’s degree in computer science or related fields (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.
• have good programming skills, preferably in Python or in C++, MATLAB, R or similar.
• have good knowledge of machine learning (ML) or statistical methods.
• documented knowledge/experience in the field of musculoskeletal health research.
• experience from epidemiological analyses of large survey or registry data.
• authored scientific articles.

Desired personal skills

• an ability to work interdisciplinary and independently.
• ability to and interest in public dissemination of research
• ability to work systematically and under pressure
• ability to finish tasks
• motivation for contribution in the field of research

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.

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:

• Cover letter, stating your reasons for applying and provide reasons for why you are particularly qualified for this Ph.D. fellowship position. The cover letter should preferably include a discussion of ML approaches you consider appropriate for predicting the specific outcomes to be addressed in this PhD-project and how you will proceed to assess the performance of the algorithms (Discussion: maximum one A4 page, size 12 Times New Roman).
• CV (maximum two A4 pages).
• The names and contact details of at least 2 references.
• All pages of all certificates/diplomas. The certificates/diplomas should include ECTS grades (A–F). Please note that a description of your country’s/university’s grading system must be included with the transcripts. This must be an official document with the seal of the university. Foreign diplomas must be translated into English by the degree-conferring institution. Education taken abroad should be recognised in advance by the Norwegian Agency for Quality Assurance in Education (NOKUT), and an authorized 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).
• List of publications, if relevant.
• 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 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.

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

For further details about the position contact:

Associate Prof. Hårek Haugerud, haugerud@oslomet.no.

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.

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

Ref.: 21/03235

Deadline for applications: 01.05.2021