Samsung Researcher/Senior Researcher Computer Vision Machine Learning 

United Kingdom, England, Cambridge 

198422095

Role and Responsibilities

We have1 positionopen 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-trainedand 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.

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 we offer:

• 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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