Enabling machines to interpret social behaviour in real life is a major frontier of Modern Artificial Intelligence (AI). The first step in developing socially intelligent systems that can help people to improve their social lives involves developing robust machine perception models. Unfortunately, there is almost no attention on developing systems to interpret behavior at high fidelity in a privacy preserving way. What could be inferred about linguistic aspects of a conversation when only non-verbal behavior is appropriate to capture at test time? One solution is to exploit high fidelity data that includes high quality audio and verbal content analysis at training time whilst using multimodal privacy preserving signals at test time. This project investigates this type of learning setting in crowded mingling events where access to high fidelity data may normally be considered too privacy invasive. It will involve research in the whole pipeline of multimodal machine perception from data collection, annotation, algorithm design, to evaluation.

This project is part of a larger ERC funded project (NEON) that develops a novel framework and learning approaches to learn narratives of intention from multiple perspectives in in-the-wild ecologically valid social situations such as social networking events.

The successful applicants will develop automated techniques to analyse multi-sensor data (video, acceleration, audio, etc) of human social behavior.

Successful candidates will work in a team of 6 on the development of artificial systems that each integrate different aspects of machine learning, multimodal sensing, ubiquitous computing and social science. In addition, the successful candidates will have the opportunity to collaborate with internationally renowned social scientists at the forefront of social machine perception with human cognition and behaviour modelling.

The candidates will also be embedded within the larger Pattern Recognition and Bioinformatics Groups comprising labs in Computer Vision, Pattern Recognition, and Socially Perceptive Computing Labs.

For information about the TU Delft Graduate School, please visit www.phd.tudelft.nl.

We are looking for applicants who have or expect to receive a Master before joining the project. Relevant topics include Social Signal Processing, Affective Computing, Natural Language Processing, Speech Processing, and Machine Learning. Experience in some or all of the following or related fields are preferred: audio/signal processing, computer vision, embedded sensing, machine learning, and pattern recognition.

• The successful applicant will have:
• good programming skills;
• curiosity and analytical skills;
• the ability to work in a multi-disciplinary team;
• motivation to meet deadlines;
• an affinity with the relevant social science research;
• good oral and written communication skills;
• an interest in communicating their research results to a wider audience;
• an aptitude for guiding students;
• proficiency in English;

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.

Fixed-term contract: 4 years.

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2770 per month in the first year to € 3539 in the fourth 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 a monthly work costs contribution. Flexible work schedules can be arranged. 

For international applicants, TU Delft has the Coming to Delft Service. This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.

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 as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Electrical Engineering, Mathematics and Computer Science

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 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.