PhD Candidate: Network Science for Green AI

Would you like to play a part in pioneering data science methods that promote sustainability and reliability in AI? Are you eager to delve into the application of network science for unlocking the secrets of the AI "black box"? Then come and join us as a PhD candidate to explore and innovate energy-efficient methods for machine learning training.

Artificial Neural Networks (ANNs) are driving new applications in AI, data science and intelligent systems. Yet, their training demands significant computational power, leading to a need for energy-efficient techniques that work well on lightweight devices. Random sparse ANNs have shown promise in addressing this challenge. However, the full understanding of these models remains unclear. Developing green AI techniques poses significant challenges, including addressing energy consumption, optimising efficiency, and ensuring scalability to meet the demands of complex tasks and large-scale deployments. As a PhD candidate, you will leverage tools from network science to tackle these challenges and gain insights into the training dynamics of ANNs.

As a PhD candidate at Radboud University, you will spearhead the frontier of machine learning innovation. Dive into crafting groundbreaking miniaturised architectures for training sparse ANNs, while delving deep into their evolution during training using cutting-edge tools from the network science domain.

You will spend roughly 10 percent of your time (0.1 FTE) helping with the teaching activities in our department. For example, you may be asked to tutor practical assignments, grade coursework, give presentations during classes, or supervise student projects.

You will be supervised by Dr Lucia Cavallaro and Prof. Tom Heskes, researchers with strong expertise in both network science and artificial intelligence.

The Institute for Computing and Information Sciences (iCIS) values a diverse workforce. Female candidates are therefore particularly encouraged to apply.

Profile

• You hold an MSc degree in computer science, data science, machine learning, mathematics, or a related field.
• You are proficient in programming languages used in scientific computing, such as Python, and you have a strong interest in further developing your programming skills.
• You might be a data scientist interested in exploring networks and graphs to solve real-world problems, a network scientist looking to specialise in data science and machine learning, or a combination of both. If you are eager to deepen your knowledge in any of these areas where you may have less experience, we encourage you to join us.
• You are fluent in verbal and written English.
• You enjoy working in a multidisciplinary environment and would like to further develop your good communication, presentation and writing skills.

We are

The Data Science group is part of the Institute for Computing and Information Sciences

(iCIS) at Radboud University. We develop theory and methods for machine learning and apply them in various fields. During recent evaluations, iCIS has been consistently ranked as the No. 1 Computing Science department in the Netherlands. Evaluation committees praise our flat and open organisational structure and our ability to attract external funding.

Our group is very friendly and welcoming; the atmosphere you will experience is relaxed and yet productive. You will be part of our gender mixed group with diverse backgrounds and cultures.

Please see Lucia Cavallaro’s Google Scholar profile for examples of the kind of research we are involved in and the techniques we use.

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.

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 (0.8 FTE 5- year contract - 1.0 FTE 4- year contract) 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) or 3.5 years (5-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 from the fourth year onwards (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.