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- Department of Physics and Astronomy, Bologna University
- Fondazione Bruno Kessler
- CMCC Foundation
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- European Institute of Oncology (IEO)
- INAF - Arcetri Astrophysical Observatory
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(multiple omics, NGSeq, Neuroimaging, text data) with Machine Learning and AI techniques. He actually leads a group with 4 PhD students (1 ITN PhD student) 3 PostDoc Students, and 3 Research Assistants, with
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(multiple omics, NGSeq, Neuroimaging, text data) with Machine Learning and AI techniques. He actually leads a group with 4 PhD students (1 ITN PhD student) 3 PostDoc Students, and 3 Research Assistants, with
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/Qualifications PhD in Computer Science or related field (with preferred specialization in Machine Learning/Deep Learning); Documented experience in Machine learning and Deep neural network model design and
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to acquire skills in innovative topics and the most widely used technologies and tools in the market. Requirements The ideal candidate should possess a master's or PhD degree in Computer Science, Computer
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computing, HPC infrastructures, and machine learning. Hicrest Laboratory combines expertise in parallel algorithms and high-performance computing with reliable computing systems by combining a hardware
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associated with different health related traits. The candidate will also apply machine learning methods to generate predictive models for these different health conditions to be applied for stratification in
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1 Mar 2024 Job Information Organisation/Company Sapienza University of Rome Research Field Computer science » Computer architecture Researcher Profile Recognised Researcher (R2) First Stage
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marker-less motion capture • Knowledge of machine learning methods • Good communication skills • Strong problem solving attitude • High motivation to learn • Spirit of innovation and
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the definition of learning cost curves. Collaborate and coordinate activities on Tecno Economics Assessment Participate and collaborate with the engineering team to validate cost and technical model. Gathering
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should have expertise in statistics, programming, and the analysis of cancer omics data. Interest in network biology and knowledge of data science techniques such as machine learning and being familiar