Position Summary
Samsung AI Centre Cambridge (SAIC-C) is a recently-established, ambitious independent research centre targeting "Human-Centric AI" to enrich peoples’ lives using AI technologies.Role and Responsibilities
We have 1 position open in the broad areas of Machine Learning and Computer Vision with a focus on Vision and Language. The positions are part of the Future Interaction Research Programme. Our topics of interest include but are not limited to:
Contrastively-trained and auto-regressive Vision & Language (e.g. CLIP, BLIP).
Visual LLMs (e.g. LLaVA).
Generative Models (e.g. Stable Diffusion and Auto-Regressive models).
Efficient Architectures.
Model Compression (distillation, quantization).
Efficient Adaptation of Large Models.
Key Responsibilities:
Conduct hands-on innovative research, including methodological conceptualization and implementation.
Publishing at top venues: CVPR, ECCV, ICCV, ICLR, NeurIPS, ICML, EMNLP, ACL, TPAMI and IJCV.
Contribute to the research agenda and directions within the center.
Interact with product and engineering teams for the purpose of technology transfer.
Skills and Qualifications
Key Skills Required:
Research experience in the fields of Computer Vision and/or Machine Learning.
Familiarity with fast prototyping Deep Learning frameworks such as PyTorch.
A track record of publishing at top-tier venues (e.g. CVPR, ECCV, ICCV, ICLR, NeurIPS, EMNLP, ACL, ICML, TPAMI and IJCV).
Ability to communicate well and to collaborate with other group members.
Experience in the topics described above, or closely related topics, is a plus but not necessary.
What SAIC-C offers:
An excellent research environment: autonomy to define a research agenda along the centre’s core lines, opportunities to collaborate with excellent researchers and to publish at top conferences.
Opportunity to transfer research into impact, and get your work reach millions of customers.
A top-tier package, on par with the top companies in the industry in Europe.
An excellent location at the heart of Cambridge, and at commute’s distance from London.
Hybrid working with 3 days onsite and 2 days working from home weekly
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