PhD Position Aarhus University Denmark

Ph.D. Position in engineering, physics, mathematics at Aarhus University, Denmark 2022

Denmark

Ph.D. Position in Using deep learning methods to tailor sleep scoring to specific populations is available for a bachelor’s or master’s degree students in engineering, physics, mathematics, Neuroscience and Biomedical science engineering at the Graduate School of Technical Sciences, Aarhus University, Denmark 2022

 

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General Info

Position: PhD
No. of Positions: 1
Research Field: , , , , ,
Deadline to Apply: Expired
Joining Date: Aug 01, 2022
Contract Period: Subject to Norms
Salary: According to standard norms

Workplace:
Graduate School of Technical Sciences

Aarhus University (AU), Denmark

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Qualification Details

The successful applicant should have a strong quantitative background (i.e., bachelor’s or master’s degree in engineering, physics, mathematics or similar), and an interest in either machine learning, neuroscience, brain computer interfaces or biomedical engineering in general. The project is expected to entail a great deal of work with conventional deep learning libraries (tensorflow, pytorch or similar), so experience with those and/or scientific programming tools (matlab, python, R, C/C++) is a definite advantage.

Given the explorative nature of scientific research, an interest and willingness to learn new skills is an important quality – perhaps even more important than the specific skillset held by the applicant at the time of application.

Our group works in a collaborative and explorative fashion. This means that good communication skills, both written and oral, are important to carrying out the everyday work.

Responsibilities/Job Description

In recent years, great progress has been made in developing algorithms for automatic sleep scoring. This is a societal good, because low cost sleep scoring will benefit many different branches of health care.

However, sleep scoring is also an interesting ‘model problem’ for developing new methods in signal analysis. Large data bases for training and testing exist, and the problem is both complex and important. An exciting challenge is the fact that training data is not equally distributed among patient groups, and some groups, in particular those that are hardest to score, have relatively little training data.

In this project, the candidate will develop and test approaches to ‘transfer learning’ or ‘semi supervised learning’, in which high performing algorithms trained on large data sets will be transformed to perform similarly well on smaller data sets for rarer types of sleep recordings.

The developed methods can be used for regular, clinical sleep recordings, but an important task will also be in testing the methods for data recorded using the “ear-EEG” platform, which has been developed in our group. In general, the methods developed in this project will likely be relevant for most mobile sleep monitoring platforms, of which there are a growing number.

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.

How to Apply?

Application Method: Online Application
Ref. No.: -

Application Procedure

For information about application requirements and mandatory attachments, please see our application guide.

Please note:

  • 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.

About the Employer:

Note or Other details

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.

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Contact details

Applicants seeking further information are invited to contact:

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