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extraction. So, from one side, there is a need for parsimonious machine learning approaches to classify, reconstruct and possibly segment 3D shapes. From another point of view, the aim of this PhD is to
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thrombosis and lung injury in Sickle Cell Disease. The prospective candidate will have the opportunity to learn state-of-the-art techniques such as Multi-Photon-Excitation intravital microscopy of the lung and
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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managing large amounts of data by designing structured databases (PostgreSQL, MySQL). Machine learning methods such deep learning for analysis of proteomics data and classification of cancer profiles. Since
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results relevant for immuno-oncology and cancer immunotherapy. Eventually these analyses pipelines will drive creation of new immune landscape scoring metrices via state-of-the-art machine learning
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sciences. Areas of interest include but are not limited to: AI-driven drug discovery and development, quantitative systems pharmacology, large language models for education and clinical support, machine
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and
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(AI/Machine Learning (ML) expert for an academic investigational position. The Department of Data Science at the Dana-Farber Cancer Institute (DFCI) and the Department of Medical Oncology seek
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information systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both