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to develop new methods, for example using machine learning. have a proven track record of independent research funding and high quality publications. have at least 5 years of post-PhD work experience
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-cell transcriptomics, or spatial tissue profiling data, and are keen to develop new methods, for example using machine learning. You have a proven track record of independent research funding and high
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, medical complexity, illness and injury. All of our work is guided by our strategic plan, Transformative Care, Inclusive World: Holland Bloorview 2030. The plan: https://strategicplan.hollandbloorview.ca
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multimodal data integration across organismal domains and data modalities, making use of state-of-the-art methodologies such as systems/network analysis, artificial intelligence and machine learning and/or
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Strong foundation in quantitative data analysis, statistics, and modeling Basic experience with machine learning or AI; ability and motivation to develop advanced skills during the PhD Knowledge
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of machine-learning models capable of analysing the potential of selected antigens as potential vaccine candidate and/or diagnostic target. You will work here The research is embedded within Wageningen
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Description REALISE - Bridging Igneous Petrology and Machine Learning for Science and Society About the REALISE Doctoral Network REALISE will train 15 Doctoral Candidates at the interface of igneous petrology
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software engines into a unified DAT toolchain, ensuring compliance with industrial EDA standards. Job requirements MSc or PhD in Electrical Engineering, Computer Engineering, or a closely related field
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required for this post. A PhD is desirable. Additional requirements Additional assets for this position are: experience in machine learning engineering and related tooling; experience with European cloud
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cases. We are particularly interested in how AI, Data Science, or Machine Learning techniques can be used to quantify and assess software and system security from open source software to cloud services