项目介绍

2024年6月8日

Are you an aspiring computer science researcher interested in deep neural networks? Do you want to understand the fundamental limits of machine learning? Then you have a part to play as a PhD candidate. By investigating the learning behaviour of neural networks from both a theoretical and experimental angle, you will help us to better understand their limits and  potentially develop improved learning algorithms.

In recent years, deep neural networks have become indispensable for all kinds of applications in image, speech and text recognition. We know how these networks can be used in practice, but the underlying theory still leaves much to be desired. For example, little is known about which tasks can and cannot be solved with a neural network, or how large a network should be for a particular task.

As part of this PhD project, you will work to increase our understanding of one or more of the following topics:
• The influence of model size on training flexibility and sensitivity to initialisation.
• The characterisation of learned neural networks among all possible networks with representable parameters.
• How networks trained from scratch differ from fine-tuned, pre-trained neural networks.
• The sensitivity of neural networks to perturbations.

These topics can be investigated from a purely theoretical angle, as well as by experiments on standard benchmarks or artificial datasets.

You will be supervised by Twan van Laarhoven.

You will spend roughly ten percent of your time (0.1 FTE) assisting with teaching activities in our department. This will typically include tutoring practical assignments, grading coursework, and supervising student projects.

Profile

• You hold a Master’s degree in Computer Science, Mathematics, Artificial Intelligence, or another relevant discipline, or you expect to obtain such a degree soon.
• You have a strong interest in fundamental research.
• You are highly motivated, open-minded, and determined to obtain a PhD degree.
• You are fluent in verbal and written English and have good communication, presentation and writing skills. 

We are

You will join the Data Science group of the Institute for Computing and Information Sciences at Radboud University. The group’s main research foci are 1) the design and understanding of deep/causal machine learning methods, 2) modern information retrieval and big data, and 3) computational immunology, with a keen eye on applications in other scientific domains, in particular healthcare, as well as industry. Our group currently consists of some 50 researchers, including 25 PhDs.

Radboud University

At Radboud University, we aim to make an impact through our work. We achieve this by conducting groundbreaking research, providing high-quality education, offering excellent support, and fostering collaborations within and outside the university. In doing so, we contribute indispensably to a healthy, free world with equal opportunities for all. To accomplish this, we need even more colleagues who, based on their expertise, are willing to search for answers. We advocate for an inclusive community and welcome employees with diverse backgrounds, cultures, and perspectives. Will you also contribute to making the world a little better? You have a part to play.

If you want to learn more about working at Radboud University, follow our Instagram account and read stories from our colleagues.

Faculty of Science
The Faculty of Science (FNWI), part of Radboud University, engages in groundbreaking research and excellent education. In doing so, we push the boundaries of scientific knowledge and pass that knowledge on to the next generation.

We seek solutions to major societal challenges, such as cybercrime and climate change and work on major scientific challenges, such as those in the quantum world. At the same time, we prepare our students for careers both within and outside the scientific field.

Currently, more than 1,300 colleagues contribute to research and education, some as researchers and lecturers, others as technical and administrative support officers. The faculty has a strong international character with staff from more than 70 countries. Together, we work in an informal, accessible and welcoming environment, with attention and space for personal and professional development for all.

We offer

• We will give you a temporary employment contract (1.0 FTE) of 1,5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4-year contract).     
• You will receive a starting salary of €2,770 gross per month based on a 38-hour working week, which will increase to €3,539 in the fourth year (salary scale P).
• You will receive an 8% holiday allowance and an 8,3% end-of-year bonus. 
• You will be able to use our Dual Career and Family Support Service. The Dual Career Programme assists your partner via support, tools, and resources to improve their chances of independently finding employment in the Netherlands. Our Family Support Service helps you and your partner feel welcome and at home by providing customised assistance in navigating local facilities, schools, and amenities. Also take a look at our support for international staff page to discover all our services for international employees.
• You will receive extra days off. With full-time employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20. 

Additional employment conditions

Work and science require good employment practices. Radboud University’s primary and secondary employment conditions reflect this. You can make arrangements for the best possible work-life balance with flexible working hours, various leave arrangements and working from home. You are also able to compose part of your employment conditions yourself. For example, exchange income for extra leave days and receive a reimbursement for your sports membership. And, of course, we offer a good pension plan. We also give you plenty of room and responsibility to develop your talents and realise your ambitions. Therefore, we provide various training and development schemes.