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relevant subject area: Machine Learning, NLP, Computer Vision or related fields. Strong publication record in AI/ML/CV or related areas. Ability to work independently and collaborate across disciplines
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or more of the following areas: (1) Generative AI and machine learning, (2) affective computing, (3) human-computer interaction or collaborative AI, and (4) interaction design, experimental design or
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on health data as well as training AI models on health data across national borders. Key responsibilities Design, implement and benchmark machine learning models for large-scale health datasets consisting
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with training and using protein language models or similar experience with non-protein large language models. Expertise in python and machine learning implementations (e.g., pytorch). Expertise in other
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, Security & Privacy, USENIX Security, CCS, etc. More generally, the project is part of a large initiative at Serval and SnT, which aims to support the reliable deployment of machine learning systems by
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establish a research profile. Develop and execute innovative research projects. Develop, train, and evaluate modern machine-learning models on GPU/HPC infrastructure. Integrate AI methods with scientific
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to [email protected] For more information about working at
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generally, the project is part of a large initiative at Serval and SnT, which aims to support the reliable deployment of machine learning systems by providing industry actors with practical evaluation tools
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, Europe and Asia. The postholder will have opportunities to develop their academic profile in data science, machine learning and statistical genetics within a friendly, accessible and internationally
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: experience working with spatial omics or spatially resolved data in tissue experience applying AI or machine learning methods to digital pathology or histology data experience analysing kidney or transplant