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PhD Research Fellow in A.I. based Tool for Memory Training for Dementia

FIRMA NIMI PEIDETUD

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Tööpakkumise number 423897

Tööpakkumise number

423897

Töö kirjeldus

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 2800 students and 240 staff members and is situated at Pilestredet Campus in downtown Oslo and at Kjeller Campus in Akershus.

PhD Research Fellow in Artificial Intelligence Based Software Tool for Memory Training for Dementia.

The computer science department at the Faculty of Technology, Art and Design currently has an opening for an interdisciplinary position at the Department of Behavioral Science, Faculty of Health Sciences. The Phd candidate will be registered the Phd program in Behavioral Science at the Faculty of Health Sciences which will be the awarding institution.

The fellowship will be for a period of four years with 25% compulsory work (teaching, lab, or project supervision responsibilities at the department).

The fellowship requires admission to the PhD-program “Behavior analysis” at the Faculty of Health Sciences. At a technical level, the research problem can be addressed by methods that have been a long-term research focus of the Networking and System Administration research group at the Computer Science department. These methods include machine learning, personalized learning, and artificial intelligence.

Area of research

The portion of the population with dementia is continuously increasing in the western world.

The aim of this project is to develop a set of tools for dementia patients in order to perform the following goals:

  • Increase the learning ability for patients with dementia, especially for important things like remembering their family members. We want to develop different methods within statistics/machine learning (personalized learning) to optimize test/learning tools procedures. In particular, input from the patients while they use the test/learning tool will be used to gradually optimize the tools.
  • The input from the test/learning tools can also be used to detect if dementia is increasing rapidly for a patient. We will evaluate different breakpoint/anomaly detection methods from the statistical/computer science literature to detect such degradations.
  • The PhD student will implement the developed testing/learning tools as an app for iPad or other "touch" platforms.

Application

The application letter shall include the applicant’s motivation for applying to this fellowship as well as a short overview of the qualifications, technical skills, experience, and personal skills that make you an attractive candidate for a successful and productive completion of this work.


Nõudmised kandidaadile

Qualifications

  • Master degree in Electrical Engineering or Computer Science (120 ECTS). Other degrees can be considered depending on qualifications.
  • Academic performance requirements:
1. Average grade for the Bachelor degree must be C or better.
2. Average grade for Subjects at Master level must be B or better.
3. The Master’s thesis must have grade B or better.
4. For persons with foreign educational background
  • Some Experience/ knowledge within machine learning artificial intelligence /personalized learning.
  • Recognized publications will be an advantage
  • Some experience with programming, preferably app programming.

The candidate must be fluent in English, both written and spoken.

Desired skills
  • Strong analytical skills
  • Excellent communication skills and ability to collaborate in interdisciplinary teams under supervision
  • Ability to work independently, take initiative, plan, dimension and direct own work
  • Some practical experience with wireless networks and/or hardware electronics will be an advantage


Ettevõte pakub

We offer

  • Opportunity to contribute to research and development of as well as collaboration across different research groups at the Computer Science Department and the Department of Behavioral Science Opportunity to collaborate with International Research Network Opportunity to collaborate with productive researchers.
  • A dynamic working environment with challenging tasks in an area of substantial development
  • Benefits such as flexible hours and various welfare schemes

Expert committee and required documentation

All application documents submitted should be in English or a Scandinavian Language.

As an applicant you will be assessed by an expert committee. In addition to the online application, you must upload the following documents:
  • application letter, CV and copies of diplomas/certificates including all grades
  • copy of MSc thesis and any other papers or publications you wish to be considered, including a full list of any publications you may have.
  • application form for the PhD programme
  • Scientific publications are not required of a PhD candidate, however, any publications will be an advantage
  • approval from ethical commitees or other relevant bodies, if applicable
  • names and contact details of 2-3 references (name, relation to candidate, email, and telephone number

Contact information

Dr. Erik Arntzen, Professor, tel: +47 67 23 64 42, email: Erik.Arntzen(at)hioa.no Dr. Anis Yazidi, assoc. Prof, tel: +47 67 23 85 95, email: Anis.Yazidi(at)hioa.no

For more information about the doctoral program, contact: kim-henrik.ruud(ä)hioa.no

Remuneration

Salary is set in accordance with the Norwegian State Salary Scale, position code 1017 PhD Candidate, wage scale 50 - 54 (429 700 - 458 800 NOK).

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. HiOA is an IA (Inclusive Workplace) enterprise and operates in compliance with the Norwegian IA agreement.



Asukoht:
Norra
Töö tüüp:
täistööaeg
Kuulutus aegub:
9. juuni 2016
Kuulutus sisestati:
18. mai 2016

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