Machine learning based biomarkers for Parkinson’s disease

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BEMÆRK: Ansøgningsfristen er overskredet

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position is available from 01 August 2024 or later. You can submit your application via the link under 'how to apply'.

Title:
Machine learning based biomarkers for Parkinson’s disease

Research area and project description:
Applicants are invited for a PhD position within biomedical machine learning.
The PhD-project is a part of a research collaboration between Department of Clinical Medicine and Department of Electrical and Computer Engineering at Aarhus University (AU). The position is available from 01 August 2024.

The aim of the PhD project is to advance machine learning methods to identify pathology specific patterns in longitudinal physiological time series. The focus is specifically on signals from a minimally obtrusive sleep monitoring device, which can monitor sleep in home-environment without assistance from healthcare professionals. This is an important tool in both basic research of the disease progression, for stratification of sub-types of the disease, and potentially in future early diagnosing and monitoring of disease progression.

The PhD is a technical-science project but will be conducted in a close collaboration between health science researchers at Department of Clinical Medicine, AU / Department of Nuclear Medicine & PET centre, Aarhus University Hospital; and biomedical engineering researchers from the Bioelectrical Instrumentation and Signal Processing group / Neuro-Technology Lab at Department of Electrical and Computer Engineering, AU.

Project description. For technical reasons, you must upload a project description. Please simply copy the project description above and upload it as a PDF in the application.

Qualifications and specific competences:
Applicants to the PhD position must have a M.Sc. degree in Electrical Engineering, Computer Engineering, Data Science, Biomedical Engineering or related field.

The candidate should have a solid background and interest in data analytics, signal processing and machine learning.

The applicant must have good oral and written communications skills and be proficient English speaker.

Place of employment and place of work:
The place of employment is Aarhus University, and the place of work is Finlandsgade 22, 8200 Aarhus N, Denmark.

Contacts:
Applicants seeking further information are invited to contact:

How to apply:
Please follow this link to submit your application.

Application deadline is Sunday 19 May 2024 at 23:59 CEST.

Preferred starting date is 01 August 2024

For information about application requirements and mandatory attachments, please see our application guide.
Please note:
  • Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be taken into account.
  • The programme committee may request further information or invite the applicant to attend an interview.
  • Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.

Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants. All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.

 

INFORMATIONER OM STILLINGEN:

- Arbejdspladsen ligger i:

Aarhus Kommune

-Virksomheden tilbyder:

-Arbejdsgiver:

Aarhus Universitet, Nordre Ringgade, 8000 Aarhus C

-Ansøgning:

Ansøgningsfrist: 19-05-2024; - ansøgningsfristen er overskredet

Se mere her: https://job.jobnet.dk/CV/FindWork/Details/6041153

Denne artikel er skrevet af Emilie Bjergegaard og data er automatisk hentet fra eksterne kilder, herunder JobNet.
Kilde: JobNet