PhD Project in Neuro-Adaptive Digital Learning

DTU Compute’s Sections for Software and Process Engineering would like to invite applications for a 3-year PhD position starting September 1st 2018. The project is financed through a DTU Alliance PhD stipend together with the University of Queensland (UQ), Australia.

Our department DTU Compute is an internationally unique academic environment spanning the science disciplines mathematics, statistics and computer science. At the same time we are an engineering department covering informatics and communication technologies (ICT) in their broadest sense. Finally, we play a major role in addressing the societal challenges of the digital society where ICT is a part of every industry, service, and human endeavour.

DTU Compute strives to achieve research excellence in its basic science disciplines, to achieve technological leadership in research and innovation, and to address societal challenges in collaboration with partners at DTU and other academic institutions, nationally and internationally, and, equally important, with industry and organizations. We communicate and collaborate with leading centres and strategic partners in order to increase participation in major consortia.

DTU Compute plays a central role in education at all levels of the engineering programmes at DTU – both in terms of our scientific disciplines and our didactic innovation.   

Project Description
The goal of this position is to develop components of a neuro-adaptive learning platform that helps to improve learning outcomes of learners by assessing the cognitive and emotional state of learners in a context-specific manner and by developing an adaptation mechanism. This position is part of a large collaborative project together with the University of Queensland.

Learners’ engagement with digital knowledge artefacts, such as conceptual models, data profiles and visualizations as well as software scripts, is increasing in multi-disciplines like data science, and exasperated by the diversity of student backgrounds entering such areas of study. Adaptive learning platforms are emerging to assist in such scenarios, however current research in adaptive learning does not provide adequate guidance on how learner engagement can be systematically measured so as to enable evidence-based ways of identifying knowledge gaps and facilitating knowledge transfer.

The usage of neuro-physiological methods and tools provides promising perspectives, since it enables the continuous and real time observation of the learner unveiling a learner’s cognitive and emotional processes while engaging in a learning task. For example, measurements of a learner’s cognitive load (e.g., using eye tracking technologies or heart rate variability) can help to pin down the challenges a learner faces in a much more fine-grained and timely manner (real-time versus post-hoc) than existing adaptive learning platforms can. Moreover, existing research emphasizes the importance of motivation and engagement for learning. There is a growing body of knowledge that point at the role of emotions as antecedents of motivation and engagement. The usage of neuro-physiological and psycho-physiological methods and tools has the potential to measure a learner’s emotional processes (e.g., galvanic skin response measures or facial gesture recognition can be used to discover if learners are frustrated or stressed) and to adapt before a learner’s motivation and engagement has dropped.

As part of the project the PhD candidate (supervised by Weber and co-supervised by Bækgaard (DTU) and Sadiq (UQ)) is expected to perform 3 one-year cycles of empirical studies supported by multi-modal data collection (including user interactions and neuro-psychological measurements) to identify a learner’s knowledge gaps and emotional responses. This will help to be better able to identify adaptation points and to refine and focus the learning pathways in the learning platform.

As part of this project the PhD candidate will closely collaborate with the PhD candidate hired at the University of Queensland. A research stay of 6 month at the partner university is planned for the PhD candidates hired both at DTU and UQ resulting into a period of co-location of 12 months.

Requirements
Candidates must have a master degree in computational science and engineering (CSE), digital media engineering (DME), or information systems engineering or equivalent academic qualifications. Preference will be given to candidates who can document experience with conducting empirical studies (in particular using neuro-physiological measurements), software development, and in addition have a background with digital knowledge artefacts such as conceptual models, data profiles and visualizations as well as software scripts. Furthermore, good command of the English language is essential.

Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in the DTU Compute PhD School Programme. For information about the general requirements for enrolment and the general planning of the scholarship studies, please see the DTU PhD Guide.  

Assessment
The assessment of the applicants will be made by Barbara Weber, Shazia Sadiq, and Per Bækgaard.

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.  

You can read more about career paths at DTU here.

Further Information
Further information concerning the project can be obtained from the principal supervisor of this project Barbara Weber, +45 9351 1664.

Further information concerning the application is available at the DTU Compute PhD homepage or by contacting PhD coordinator Lene Matthisson +45 4525 3377.

Application
Applications must be submitted in English as one single PDF, and we must have your online application by 15 June 2018 (local time). Please open the link in the red bar in the top of the page: “apply online” (“ansøg online”).

Applications must include: 

    application (letter of motivation)CVdocumentation of a relevant completed M.Sc. or M. Eng.-degreecourse and grade list of bachelor and master degreesExcel sheet with translation of grades to the Danish grading system (see guidelines and excel spreadsheet here)

Candidates may apply prior to ob­tai­ning their master’s degree, but cannot begin before having received it.

All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.

DTU Compute has a total staff of 400 including 100 faculty members and 130 Ph.D. students. We offer introductory courses in mathematics, statistics, and computer science to all engineering programmes at DTU and specialised courses to the mathematics, computer science, and other programmes. We offer continuing education courses and scientific advice within our research disciplines, and provide a portfolio of innovation activities for students and employees.

DTU is a technical university providing internationally leading research, education, innovation and scientific advice. Our staff of 6,000 advance science and technology to create innovative solutions that meet the demands of society, and our 11,200 students are being educated to address the technological challenges of the future. DTU is an independent academic university collaborating globally with business, industry, government and public agencies. 
Deadline: 15 June 2018
Unit: DTU Compute
Read the job description and apply online

Post expires on Friday June 15th, 2018