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computer vision techniques, transformer architectures, and multi-modal learning. Familiarity with reinforcement learning (RL) principles, curriculum learning strategies, and the challenges of real-time
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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themes: (a) learning efficiency, computational creativity (zero, few-shot, and long-tail learning of 2D and 3D vision tasks. This also includes efficient generative models that are capable of generating
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programme. Details of research projects currently being undertaken can be seen at: https://www.crick.ac.uk/vivian-li/ . Key Responsibilities In this project, some of the specific aims include but are not
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computer vision techniques, transformer architectures, and multi-modal learning. Familiarity with reinforcement learning (RL) principles, curriculum learning strategies, and the challenges of real-time
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samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize, and validate a refractive
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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comprising computational biologists, bioengineers, and immunologists. The candidate will have access to advanced platforms for single-cell and spatial omics, 3D tissue modeling, bioreactors, and in vivo models
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of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 15 Mar 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number EU-59003