TU Delft is a top tier university and is exceedingly active in the field of Artificial intelligence. The Delft Tensor AI Lab (DeTAIL) is devoted to the development and application of novel tensor-based AI methods for biomedical signal processing and pattern recognition problems. Real-life biomedical data is often high-dimensional. Current solutions artificially segment such high-dimensional data into shorter one- or two-dimensional arrays, causing information loss by destroying correlations between these data sets. At the same time, advances in biomedical sensor and imaging technology – such as substantially larger recording durations of wearable sensor technology and the unprecedented increase in spatial and temporal resolution of the latest neuroimaging techniques – have led to ever increasing data sets. Tensors (multi-dimensional arrays) are the data structure of choice in artificial intelligence research to exploit the full potential of such large-scale data in a timely manner without loss of accuracy.

DeTAIL has 1 PhD vacancy: Epileptic seizure prediction using tensor-based supervised learning
Despite ongoing research efforts involving academia and industry in large international collaborations, epileptic seizure detection and especially prediction is still an unsolved problem. The key to the solution may lie within ultralong-term, real-life datasets that are currently being generated using wearable sensors. The most promising datasets include multiple sources of relevant information besides brain activity, such as cardiac activity, motion, or the circadian rhythm. The goal of your research is to find suitable methods to 1. Combine multichannel EEG and other data modalities 2. Learn the non-linear mapping between the different modalities and the non-linear separation between classes 3. Forecast seizures and at the same time quantify the uncertainty of a prediction.

The research will be carried out in close collaboration with 3 other PhD students in the lab, working on

(1) Tensor-based source separation of functional ultrasound data

(2) Large-scale tensor-based kernel methods

(3) Learning nonlinear dynamics from uncertain data with tensors

• An MSc degree in an engineering discipline relevant to PhD research.
• Demonstrated competences in one or more of these categories: numerical linear algebra, statistics, machine learning, signal processing, biomedical engineering, or another relevant field.
• An affinity with teaching and guiding students.
• A proven record and interest in further developing your modelling, programming, analytical and scientific writing skills.
• An affinity with biomedicine and neuroscience.
• Proficiency in expressing yourself verbally and in writing in English.
• The ability to work in a team, take initiative, are results oriented and systematic

Fixed-term contract: 5 jaar.

TU Delft offers DAI-Lab PhD-candidates a 5-year contract (as opposed to the normal 4-years), with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2395 per month in the first year to € 3217 in the fifth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health

insurance and sport memberships, and a monthly work costs contribution. Flexible

work schedules can be arranged. For international applicants we offer the Coming to

Delft Service and Partner Career Advice to assist you with your relocation

Delft University of Technology

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context. At TU Delft we embrace diversity and aim to be as inclusive as possible (see our Code of Conduct). Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale.

Challenge. Change. Impact! 

Faculty Electrical Engineering, Mathematics and Computer Science

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three disciplines - electrical engineering, mathematics and computer science. Combined, they reinforce each other and are the driving force behind the technology we use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make future-proof. We are also working on a world in which humans and computers reinforce each other. We are mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. There is plenty of room here for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1,100 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

DeTAIL is a Delft Artificial Intelligence Lab. Artificial Intelligence, Data and Digitalisation are becoming increasingly important when looking for answers to major scientific and societal challenges. In a DAI-lab, experts in ‘the fundamentals of AI technology’ along with experts in ‘AI challenges’ run a shared lab. As a PhD, you will work with at least two academic members of staff and three other PhD candidates. In total TU Delft will establish 24 DAI-Labs, where 48 Tenure Trackers and 96 PhD candidates will have the opportunity to push the boundaries of science using AI. You will be a member of the thriving DAI-Lab community that fosters cross-fertilization between talents with different expertise and disciplines.

Each team is driven by research questions which arise from scientific and societal challenges, and contribute to the development and execution of domain specific education. You will receive a 5-year contract and will be deployed for AI-related education for the usual teaching effort for PhD students in the faculty plus an additional 20%. The extra year compared to the usual 4-year contract accommodates the 20% additional AI, Data and Digitalisation education related activities. All team members have many opportunities for self-development.

The DAI Lab DeTAIL is led by Borbála Hunyadi and Kim Batselier. You will work in the faculty of Electrical Engineering, Mathematics and Computer Science

(EEMCS), Department of Microelectronics, Circuits and Systems Group.

The Faculty of EEMCS makes vital contributions to global welfare, well-being, and security. Its Health and Wellbeing theme involves a wide diversity of researchers from within the faculty. Their involvement ranges from microelectronic devices for human organ and disease models to mathematical biophysics, and from implantable medical devices to genomic data analysis and visualization. The theme is well embedded into the TU Delft DRI “Medical Delta”, and many regional (LDE) and national (NKI, VUMC, Hubrecht Institute, Dutch Burn Centre, LUMC, 4TU) collaborations exist. The aim is to contribute to faster and more accurate diagnostics, advanced therapy, improved health-related quality of life (also for healthy people, to improve productivity and overall societal participation), and better prevention, care and cure, at reduced cost. In the Department of Microelectronics, the design and implementation of biomedical microsystems address challenges such as high-quality signal modeling, miniaturization, accuracy and reliability, energy efficiency, biocompatibility, manufacturability and cost. Research encompasses material and technology, device and circuit design, signal processing, system implementation and software design.