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Post Doc in machine learning for biomedical time series and sleep, Department of Electrical and Computer Engineering, Aarhus University

Aarhus Universitet (AU)



Applications are invited for a 1 year post doctoral position in the field of machine learning and biomedical engineering, working on the problem of unsupervised learning for sleep scoring. The work will take place at Department of Electrical and Computer Engineering, Aarhus University, Denmark.
Expected start date and duration of employment
This is a 1–year position from 1st of December 2024 or as soon as possible.
Job description
You will be part of an ongoing project on updating how sleep is monitored clinically. This is a time series classification problem with large societal value, and a particularly interesting open problem is how to implement semi- or unsupervised approaches, since data is cheap and labels are not.

We recently found a very promising approach to this problem, and believe that even within the relatively short duration of this position, good results can be achieved with many possible follow-up questions and impact-full publications.

The work will take place in the context of the Center for ear-EEG, which includes multiple projects on wearable sleep monitoring. As such, even though the project is entirely machine learning based, it is very much possible to have the work motivated and justified through the other projects in the center.

The main tasks in the position are:

  • Familiarize yourself with our existing work on this problem
  • Together with Associate Professor Kaare Mikkelsen draw up a prioritized list of likely improvements to the existing method.
  • Implement the improvements
  • Characterize the performance of the new version of the method.
  • Prepare a manuscript about the method for publication in a suitable journal.

Your profile
Applicants should have a PhD degree in a technical field (engineering, physics, computer science or similar) and have experience with machine learning and python. It is a great advantage to already be familiar with the Pytorch framework or at least neural networks in general.

It is a benefit to have experience with sleep research or biomedical data, though not a requisite. Experience with transfer learning, supervised or otherwise, is also advantageous, though not required.

The candidate should have good communication skills in either English or Danish and be able to work independently.
Who we are
The ‘Biomedical Machine Learning’ (BIML) group is a small group affiliated with the larger ‘Center for ear-EEG’, located at Department of Electrical and Computer Engineering, Aarhus University.

Both the department and the center are highly international – at the moment, the center houses people from six different nationalities.

In the BIML group, the focus is particularly on applied machine learning research relevant for biomedical data. This could be upscaling of MRI data, or automatic sleep scoring of wearable EEG.
Group webpage is found here: https://ece.au.dk/biml

Read more about the center for ear-EEG here:
https://ece.au.dk/en/research/research-centres/center-for-ear-eeg
What we offer

  • A well-developed research infrastructure, laboratories and access to shared equipment
  • An interdisciplinary environment with national, international and industrial collaborators
  • A research climate encouraging lively, open and critical discussion within and across different fields of research
  • A workplace characterised by professionalism, equality and a healthy work-life balance.

Place of work and area of employment
The place of work is Finlandsgade 22, 8200, Aarhus N, and the area of employment is Aarhus University with related departments.
Contact information
For further information, please contact: Assoc. Prof., Kaare Mikkelsen, +45 60612038, [email protected].
Deadline
Applications must be received no later than 18th of September, 2024.

Application procedure
Shortlisting is used. This means that after the deadline for applications – and with the assistance from the assessment committee chairman, and the appointment committee if necessary, – the head of department selects the candidates to be evaluated. All applicants will be notified whether or not their applications have been sent to an expert assessment committee for evaluation. The selected applicants will be informed about the composition of the committee, and each applicant is given the opportunity to comment on the part of the assessment that concerns him/her self. Once the recruitment process is completed a final letter of rejection is sent to the deselected applicants.
Letter of reference
If you want a referee to upload a letter of reference on your behalf, please state the referee’s contact information when you submit your application. We strongly recommend that you make an agreement with the person in question before you enter the referee’s contact information, and that you ensure that the referee has enough time to write the letter of reference before the application deadline.
Unfortunately, it is not possible to ensure that letters of reference received after the application deadline will be taken into consideration.
Formalities and salary range
Technical Sciences refers to the Ministerial Order on the Appointment of Academic Staff at Danish Universities under the Danish Ministry of Science, Technology and Innovation.

The application must be in English and include a curriculum vitae, degree certificate, a complete list of publications, a statement of future research plans and information about research activities, teaching portfolio and verified information on previous teaching experience (if any). Guidelines for applicants can be found here.

Appointment shall be in accordance with the collective labour agreement between the Danish Ministry of Taxation and the Danish Confederation of Professional Associations. Further information on qualification requirements and job content may be found in the Memorandum on Job Structure for Academic Staff at Danish Universities.

Salary depends on seniority as agreed between the Danish Ministry of Taxation and the Confederation of Professional Associations.

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.

Research activities will be evaluated in relation to actual research time. Thus, we encourage applicants to specify periods of leave without research activities, in order to be able to subtract these periods from the span of the scientific career during the evaluation of scientific productivity.

Aarhus University offers a broad variety of services for international researchers and accompanying families, including relocation service and career counselling to expat partners. Read more here. Please find more information about entering and working in Denmark here.

Aarhus University also offers a Junior Researcher Development Programme targeted at career development for postdocs at AU. You can read more about it here.

The application must be submitted via Aarhus University’s recruitment system, which can be accessed under the job advertisement on Aarhus University's website.

Aarhus University
Aarhus University is an academically diverse and research-intensive university with a strong commitment to high-quality research and education and the development of society nationally and globally. The university offers an inspiring research and teaching environment to its 38,000 students (FTEs) and 8,300 employees, and has an annual revenues of EUR 935 million. Learn more at www.international.au.dk/

Department: Biomedical Engineering/Biomedical Engineering

Deadline: 18 September 2024

Location: Aarhus N Finlandsgade 22

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Aarhus Universitet (AU)

Nordre Ringgade 1, 8000 Aarhus C

Aarhus Universitet blev grundlagt i 1928 og er i dag i den absolutte verdenselite på flere forskningsfelter. Aarhus Universitet (AU) er blandt verdens 10 bedste universiteter grundlagt inden for de seneste 100 år og har en lang tradition for partnerskaber med nogle af verdens bedste forskningsinstitutioner og universitetsnetværk.

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