187 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions in Sweden
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different backgrounds. This position requires that you have graduated at Master’s level in in computer science, media technology, computer engineering, human-computer interaction, visual learning and
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of full-time. Your qualifications You have graduated at Master’s level in biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered
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identification and machine learning is a merit. What you will do Perform research, developing your own scientific concepts and communicating the results verbally and in writing Take courses at an advanced level
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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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addition to conventional software, the scope includes engineering of AI enabled systems (primarily ML and LLM), and thus MLOps (Machine Learning Operations), datacentric AI, and legal and ethical aspects of AI
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data
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a strong constellation of both traditional applied biostatistics and expertise in artificial intelligence and machine learning, which is undergoing rapid development. The clinical activities
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testing and collaboration with infrastructure owners or managers - Experience in supervision - Knowledge of data-driven methods, signal processing, or machine learning - Familiarity with sustainable
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support the teaching activities courses at KTH and further develop methodologies and algorithms for the quantum computer simulators. Qualifications Requirements A graduate degree or an advanced level
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) and Machine Learning/NLP (Natural Language Processing) to capture both the network embeddedness and the qualitative B2B relationship features of supply chains. The project identifies key bottlenecks