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degree (PhD or equivalent) in computer science, data science, statistics, bioinformatics, or a related discipline A strong publication record in machine learning, computer science, bioinformatics
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analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive
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, calibration, and the development of analysis tools and software. Our key focus areas are the physics of jets, top quarks, and EWSB, including the development of novel machine-learning methods for high-energy
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Devices (QCD) group at the Department of Applied Physics. In this position you have a chance to make history by demonstrating some of the first experiments of the future quantum-computer technology that is
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, normalization, dimensionality reduction) to downstream interpretation (differential expression, gene set enrichment, and cell type annotation). Implement Machine Learning Approaches, including deep learning
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at the University of Helsinki. The main research fields at the department are artificial intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms
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. We require the candidate to have documented experience in either large-scale genomics data analysis with computational or approaches/biostatistics, or machine learning/deep learning. Experience with
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research / molecular biology. The proposed project is intended for an MSc-level person who wishes to acquire PhD degree. PhD Trainees in my laboratory typically graduate within 3-4 years with competitive
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will be working closely with machine learning researchers and on scientific projects using AI, so a background in machine learning is highly desirable. In general, candidates should have a doctoral
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The Faculty of Science invites applications for a POSTDOCTORAL RESEARCHER IN MACHINE LEARNING FOR NATURE CONSERVATION starting from August 2025 or as agreed. The Postdoctoral Researcher will be