PhD position on Sparse Training for Energy-Efficient Deep Learning

The main goals of this PhD project are:

• Develop novel sparse training algorithms that improve the scalability and energy efficiency of deep neural networks.
• Investigate the mathematical underpinnings of sparsity in deep learning and its effects on learning dynamics, and generalization.
• Implement and benchmark sparse training methods to scale up deep learning.
• Publish and present research findings in top-tier conferences (e.g., NeurIPS, ICLR, ICML, IJCAI, AAMAS, ECMLPKDD) and journals (e.g., Machine Learning, JMLR).
• Collaborate with an international team of researchers and industry partners.

The successful candidate will be embedded in the DMB research group, and the supervision will be ensured by Dr. Elena Mocanu and Prof.dr. Maurice van Keulen. This PhD position is part of the Modular Integrated Sustainable Datacenter (MISD) project and will have ample collaboration opportunities. As part of the MISD project effort led by Elena Mocanu, we are opening multiple positions (two Ph.D. candidates and one PostDoc) to join us and work at the interplay of dynamic sparse training in neural networks on various tasks.

Useful links:

Are you interested in this position? Please send your application via the 'Apply now' button below before 20 May 2024, and include:

• A brief motivation letter (maximum 2 pages), emphasizing (a) your individual reasons for desiring this role, (b) a reflective evaluation of your most and least developed skills (optional), and (c) your personal research interests and goals (optional).
• A full Curriculum Vitae, including your contact details, educational background, work experience (if any), publications (if any), and English proficiency test scores (optional).  
• Certified copies of degree certificates, with an accompanying detailed list of courses completed and corresponding grades.
• Names and contact details of 2-3 referees (they will be approached only if the candidate is shortlisted).

For more information regarding this position, you are welcome to contact Dr Elena Mocanu ([email protected])

Our DMB collective stands by its diversity, inclusivity, and interdisciplinary composition. We are doing research at the forefront of advancements in machine learning, deep learning, and computer vision to advance scientific knowledge and societal welfare in a large spectrum of data science applications. We disseminate our research findings through publications in leading conferences (such as NeurIPS, ICLR, ICML, AAMAS, and CVPR) and prestigious journals (e.g. Nature Communications, Machine Learning, etc.).

The faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) uses mathematics, electronics and computer technology to contribute to the development of Information and Communication Technology (ICT). With ICT present in almost every device and product we use nowadays, we embrace our role as contributors to a broad range of societal activities and as pioneers of tomorrow's digital society. As part of a people-first tech university that aims to shape society, individuals and connections, our faculty works together intensively with industrial partners and researchers in the Netherlands and abroad, and conducts extensive research for external commissioning parties and funders. Our research has a high profile both in the Netherlands and internationally. It has been accommodated in three multidisciplinary UT research institutes: Mesa+ Institute, TechMed Centre and Digital Society Institute.