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, biofluids) data sources from primary or secondary sources. Applies a range of statistical, computational, and machine learning methods to research data. Provides guidance and training to junior analysts
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machine learning (ML)) and emerging data types (electronic health records (EHR), biobanks and disease registries, and next-generation genomics including single-cell and spatial omics); communicates clearly
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focuses on single-cell genomics, biotechnology, and bioinformatics. The project involves transcriptomic and genomic profiling of single microbes. The post-doc will work on machine and deep learning methods
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command of written and spoken English • Experience with qualitative research methods is an asset • Good knowledge of machine learning /data mining in science • Good programming skills in at least one
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or information and communication technology, 2) experience in teaching, 3) experience in machine learning, deep learning, particularly in the application of biomedical data processing, 4) experience in processing
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
(as machine learning techniques, etc.). Personal characteristics In the evaluation of which candidate is best qualified for the PhD position, emphasis will be placed on education, experience and
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following thematic areas: • AREA 1: Machine learning and AI-driven methods for design, simulation, and optimisation in architectural and construction engineering. • AREA 2: Robotic and additive
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Accounting, Computer Literacy, Deadline Management, Grant Administration, IBM Cognos Analytics, Interpersonal Communication, Learning New Technologies, Maintaining Composure, Microsoft Excel, Microsoft Word
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Python Arduino and C++ (Physical Computing) Creation of interactive objects and components Machine Learning and Natural Language Processing Specific Requirements Candidates must hold a PhD in engineering
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values align with our Guiding Values and Principles , Principles of Community , and Strategic Plan . At UC Berkeley, we believe that learning is a fundamental part of working, and provide space for