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<p class="MsoNormal"><b><span lang="EN-US">Applications are invited for a PhD fellowship/scholarship at Graduate School of Natural Sciences, Aarhus University, Denmark, within the Computer Science programme. The position is available from May 2024 or later.<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Title:</span></b><span lang="EN-US"> Progressive Visual Analytics for EEG Data<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Application Deadline:</span></b><span lang="EN-US"> February 01, 2024<o:p></o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Call & Application Information: </span>
</b><span lang="EN-US"><a href="https://phd.nat.au.dk/for-applicants/open-calls/february-2024/progressive-visual-analytics-for-eeg-data">https://phd.nat.au.dk/for-applicants/open-calls/february-2024/progressive-visual-analytics-for-eeg-data</a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Research area and project description:<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US">A key challenge in the computational analysis of EEG (Electroencephalography) data lies in identifying and mitigating artifacts—distortions in the signals that span spatial, frequency, and temporal scales. Defining what
constitutes an artifact and determining the threshold for normal data is inherently complex and often de-pends on the method of data collection. As a result, semi-automated, human-in-the-loop approaches are commonly employed. These methods pre-screen EEG da-ta
automatically for artifacts, and domain experts subsequently correct the output. While effective, this process is time-consuming and labor-intensive.<br>
This PhD project sets out to revolutionize the current artifact detection paradigm by integrating the results of multiple detection methods running in parallel. The key innovation lies in proposing Multiplex Analytics—a concept designed to progressively visualize
the stream of multiple, automated detection results. This approach allows experts to monitor and steer the detection processes effectively.<br>
It will be your task to first and foremost develop a platform to run such multiplex analyses, to invent novel methods for fusing and displaying their results, to make the multiplex interactively steerable by the human analyst, and to apply this new analysis
paradigm to the use case of EEG anomaly detection.<br>
Work on this PhD topic will be conducted as part of the ArtiPlex project funded by the Independent Research Fund Denmark (Danmarks Frie Forskningsfond). Within this project, you will collaborate closely with EEG specialists at Aarhus University’s Department
of Engineering, as well as with researchers from abroad working in the emerging field of Progressive Visual Analytics.<br>
Please upload a project description (½-4 pages). This document should describe your ideas and research plans for this specific project. If you wish to, you can indicate an URL where further information can be found.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Qualifications and specific competences:<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US">To apply for the position, you must have a relevant Master’s degree (or equivalent) and excellent computer programming skills. Prior experience in at least one of the following areas is of advantage: data visualization,
data science, computer graphics, human-computer interaction, or concurrent programming. You are expected to bring or develop the necessary soft skills for working in teams, as well as for managing and communicating your research progress. The same holds for
the necessary hard skills in software development and scientific writing.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Place of employment and place of work:<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US">The place of employment is Aarhus University, and the place of work is Department of Computer Science, Åbogade 34, 8200 Aarhus N, Denmark.
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Contacts:<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US">Applicants seeking further information for this project are invited to contact: Associate Professor Hans-Jörg Schulz,
<a href="mailto:hjschulz@cs.au.dk">hjschulz@cs.au.dk</a><o:p></o:p></span></p>
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