PhD Candidate: Network Science for Green AI

4 weken geleden


Nijmegen, Nederland Radboud University Voltijd

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.



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