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Field
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Your Job: Machine Learning (ML) and artificial intelligence (AI) based on neural networks are currently reshaping all aspects of society. In several areas, such as medicine, AI-based tools
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a soldering station, hardware tools, as well as using a computer You are willing to expand your knowledge to cover the entire range of sample environment at JCNS You are a good team-player when it
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workflows within SolMates development of scalable, cloud-based workflows, including the integration of machine learning components into Temporal-based workflow orchestration for data processing, analysis, and
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RPTU University Kaiserslautern-Landau • | Kaiserslautern, Rheinland Pfalz | Germany | about 1 hour ago
environment of the transregio, one of the leading thoughts is the implementation of the general ''Problem-Based Learning'' principle, i.e., to combine the studies with hands on experience in research wherever
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datasets with machine learning methods, and software development are beneficial Good organisational skills and ability to work systematically, independently and collaboratively Effective communication skills
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science (for example, but not limited to, data science, machine learning, statistics, computer science, mathematics, etc.) acceptance by a PhD supervisor with examination admission for PDS and formation
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Infrastructures Didactics of Informatics Digital Humanities Distributed Systems High-Performance Storage Machine Learning Medical Informatics Neural Data Science Practical Informatics Scientific Information
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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Description For our location in Hamburg we are seeking: Doctoral Researcher in Machine Learning and Data Processing in the Field of Seismic Measurements Remuneration Group 13 | Limited: 3 years
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for biotechnology. By combining mecha-nistic, statistical, and machine-learning models with automated experimental execution, the project will enable traceable, reproducible, and metadata-rich experimental planning