Postdoc position on Scalable Energy-Efficient Deep Learning
University of Twente is at the forefront of computational research, committed to advancing the field of artificial intelligence with a focus on sustainability. Our team is dedicated to pioneering scalable and energy-efficient deep learning algorithms that can pave the way for the next generation of eco-friendly AI technologies. We are seeking a highly motivated Postdoctoral Researcher to join our dynamic team.
The successful candidate will be involved in cutting-edge research aimed at developing scalable sparse deep learning models that are not only powerful but also energy-efficient. This position offers the unique opportunity to contribute to high-impact projects, collaborate with world-renowned experts in the field, and publish in top-tier journals and conferences.
Key Responsibilities:
- Design and implement scalable, energy-efficient deep learning algorithms.
- Conduct rigorous experimental evaluations to benchmark the performance and energy efficiency of the developed models.
- Collaborate with interdisciplinary teams to apply these models to real-world problems in areas such as natural language processing, computer vision, and more.
- Publish and present research findings in leading scientific journals (e.g., Machine Learning, JMLR) and conferences (e.g., NeurIPS, ICLR, ICML, IJCAI, AAMAS, ECMLPKDD).
- Contribute to the mentoring of graduate students and junior team members.
The Postdoctoral Researcher will be embedded in the DMB research group headed by Prof. dr. Maurice van Keulen and the supervision will be ensured by Dr. Elena Mocanu. This 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:
- Elena Mocanu webpage
- DMB research group
- MISD project
- Sample of our work on sparsity
Information and application
Are you interested in this position? Please send your application via the 'Apply now' button below before 20 May 2024, and include:
- A detailed CV including a list of publications.
- A brief cover letter (maximum 2 pages) highlighting your research interests and suitability for the position.
- 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 Elena Mocanu (e.mocanu@utwente.nl)
About the department
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.).
About the organisation
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.
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