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are seeking an experienced and highly skilled Data Scientist with a strong foundation in genomic biostatistics to join our team. This role involves leveraging advanced statistical methods and machine learning
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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reclamation pilot-scale and lab-scale systems. Conduct membrane and separation process modelling, module-scale desalination system modelling, including conventional modelling and machine learning-based
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. Excellent experience in membrane and separation process modelling, module-scale desalination system modelling, including conventional modelling and machine learning based modelling. Relevant research
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-resolution (SR) technologies influence human and machine-based facial identification. The PhD will combine behavioural experiments, machine learning, and explainable-AI methods to answer questions: 1. Do SR
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 2 days ago
or related field # Numerical solution of partial differential equations # Scientific computing / high-performance computing # Algorithmic modeling and simulation # Fundamentals of machine learning or quantum
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Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau | Landau in der Pfalz, Rheinland Pfalz | Germany | 7 days ago
to develop the research of the position holder into adjacent areas such as computer science, artificial intelligence, or machine learning. Existing or planned involvement in at least one of these areas until
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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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into actionable insights, novel tools, and impactful research outcomes. Key Responsibilities Develop, implement, and optimise AI/ML models (artificial intelligence/classical machine learning, deep learning
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the project: Develop, train, and optimise deep learning models for wildlife species identification, classification, and segmentation using real-world datasets. Design and implement software modules to integrate