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PhD scholarship in Magnetothermal energy harvesting from waste heat - DTU Energy

Do you want to contribute to a future where we can harvest energy from waste heat using a novel magnetothermal device, that you will get to help realize? If yes, this PhD-position is exactly for you.

Your role as a PhD student will be to design, model and build an experimental magnetothermal energy harvesting device setup where we can test new designs of the various sub-components of the systems. The project has many aspects. You will work on building the device and testing this, but working on a numerical model of the system is also a potential option.

PhD scholarship in Efficient Numerical Modelling of Smart Hearables while Fitted to Users – DTU Electro

Are you interested in learning and extending on state-of-the-art numerical modelling techniques and, at the same time, contribute to improving the listening experience of smart hearable users and hearing impaired?

As a PhD candidate with us you will create models of audio devices that are worn in or over the ear and including the wearer’s acoustical and mechanical influence. You will deal with a large modelling task with uncertain input data, and to do this you will acquire knowledge on fields like advanced numerical modelling techniques, improvement of model efficiency and numerical optimization, among others.

PhD Scholarships (2) in Cyber-Security and Cyber-Deception – DTU Compute

The two positions are part of the project Loki: Situational aware collaborative bio-inspired cyber-deception.

This project, inspired by Norse mythology, with Loki being a shape-shifter god and a master of trickery, aims at redefining and evolving the emerging field of cyber-deception. Here, we attempt to deceive attackers by creating fake vulnerable systems that are aware of their surroundings and are constantly shifting.

The project takes inspiration from nature (e.g., from the mimicry phenomenon) to synthesize sophisticated deception.

Industrial PhD and Postdoc Positions in Machine Learning for Hearing Aid Systems

The projects will explore and design machine learning algorithms for processing potentially noisy sound signals in a hearing aid context, to increase speech intelligibility and sound quality, and to reduce listening effort.

The algorithms to be explored rely on hearing aid microphone signals, as well as additional multi-modal inputs, e.g., EEG/Ear-EEG, visual inputs, user body movements, and eye-tracking. We aim to develop algorithms which align with the user’s intent by continuously providing the sound signal of interest to the user.