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PhD Project in Active Deep Learning for Nano Sensor Systems

DTU Compute's Sections for Cognitive Systems, would like to invite applications for a 3-year PhD position starting April 1st 2017, or as soon as possible. The project is part of IDUN center of excellence (www.idun.dtu.dk), which is a multi-disciplinary lead by DTU Nanotech with participation of DTU Compute and Copenhagen University,and funded by the Danish National Research Foundation and the Villum Foundation. The center is divided into IDUN Drug and IDUN Sensor that focuses on drug delivery and nano-mechanical sensors, respectively.

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 PhD project is associated with IDUN Sensor research that focuses on development and exploration of nano-mechanical biosensors. Data processing and modelling are indispensable multipurpose tools for sensor development and evaluation and analysis of results in demonstration activities. In sensor development, data modelling tools provide other views and insight into the physical and chemical properties of the sensor as well as sensing principle; hence, improving sensor development in terms of time-use, but also the ability to robustly confirm hypotheses about the sensor's functionality. In relation to sensor demonstration activities, data modelling is important for obtaining robust sensor performance by suppressing of noise caused by undesired physical and chemical properties of the sensor as well as uncontrollable experimental factors.

Recent advances within the deep learning field has shown remarkable performance in a great variety of data processing tasks. The PhD project will focus on developing new active learning methods for deep neural network models. Such methods can provide optimal experimental design and hypothesis testing for sensor development, and further reduce the need for user labels in connection with demonstration of detection and predictive sensing capabilities. The methodological research relates to, and will leverage from, current advances in Bayesian optimization; one-shot-learning; generative adversarial networks; and users-in-the-loop models, where the user is the sensor developer and/or an end-user providing labeled information.

Requirements

Candidates must have a master degree in either machine learning, computational science and engineering, applied mathematics, engineering, or equivalent academic qualifications. Preference will be given to candidates who can document knowledge in machine learning, neural networks, and sensor information processing and in addition have a background and experience with Bayesian statistics, experimental design and sensor systems. 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 Professor Jan Larsen.

We offer

We offer an interesting and challenging job in an international environment focusing on education, research, scientific advice and innovation, which contribute to enhancing the economy and improving social welfare. We strive for academic excellence, collegial respect and freedom tempered by responsibility. The Technical University of Denmark (DTU) is a leading technical university in northern Europe and benchmarks with the best universities in the world.

Salary and terms of employment

The salary and appointment terms are consistent with the current rules for PhD degree students. The period of employment is 3 years.

Further Information

Further information concerning the project can be obtained from Professor Jan Larsen, +45 45253923, janla@dtu.dk and Senior Researcher Tommy S. AlstrÝm, +45 45253431, tsal@dtu.dk. Further information concerning the application is available at the DTU Compute PhD homepage or by contacting PhD coordinator Lene Matthisson +45 4525 3377. Application

Apply online at www.career.dtu.dk. Applications must be submitted in English as one single PDF, and we must have your online application by March 1, 2017. 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)
  • CV
  • documentation of a relevant completed M.Sc. or M.Eng.-degree
  • course and grade list of bachelor and master degrees
  • Excel 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.
 

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