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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics
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reporting. Understand how data flows through EDW, ODS, and data marts. Learn fundamentals of dimensional modeling and data lineage. Develop precision, documentation habits, and professional communication
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chemistry modelling techniques scientific machine learning high-performance computing molecular design, generative AI, database handling and analysis collaborative, project management, presentation and
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FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria - Thesis in natural language processing with machine learning, - mastery of NLP and machine learning methods and tools
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modalities Experience with signal enhancement, machine learning, or data-driven imaging analysis Track record of publications in high-impact scientific journals, conferences or patents. Experience contributing
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The Singapore Institute of Technology (SIT) is Singapore’s first University of Applied Learning and the third largest university by intake in Singapore. Our mission is to maximise the potential
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to work in a variety of learning environments, including classrooms, libraries, computer labs, and event spaces.Ability to communicate clearly in person, over the phone, and via electronic platforms.Must be
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 17 days ago
skills. Have very good knowledge of machine-learning and data science methods, especially for timeseries data Have very good programming skills in programming languages such as Python. Have previous
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transitions in and out of campus housing, accurate data reporting, and collaborative partnerships across departments. As part of our integrated residential education model, you’ll work closely with professional
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early detection is a need that needs to be addressed using advanced sensors. The candidate will apply machine learning and IA methods to anticipate the evolution of the discharges. This project aims