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Field
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optimization Close collaboration with synchrotron groups and in-house scientists for design, commissioning, and operation of experiments Collaboration with the INW-1 machine learning team on data handling
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essential. Good programming skills in at least one programming language (e.g., Python). Experience with machine learning, LLMs, or HCI/user study methodologies will be a plus. Strong interest in acquiring and
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, as well you know how to work with a computer You work as a team player to solve problems analytically and work out solutions You are willing to learn something new every day and are willing to participate
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Cluster of Excellence Machine Learning Cluster of Excellence TERRA CIN LEAD Graduate School & Research Network Collaborative Research Centers Transregional Collaborative Research Centers (CRC-TRRs) DFG
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | about 2 months ago
of population processes is key. Examples of themes that we are interested in strengthening include (but are not limited to): The use of advanced statistical analysis, machine learning or causal inference
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Max Planck Institute for Demographic Research, Rostock | Rostock, Mecklenburg Vorpommern | Germany | 3 months ago
strengthening include (but are not limited to): The use of advanced statistical analysis, machine learning or causal inference for estimating, understanding and forecasting demographic and health outcomes
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) department develops innovative deep learning technologies in the area of image and video analysis. The department's competencies cover the entire processing chain, from the collection and analysis of visual
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of the professorship is on aspects of high dimensionality using geometric and probabilistic methods, with applications in, e.g., data science, machine learning or quantum computing. Proven expertise in areas such as
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computing, from quantum chemistry and optimization to machine learning (ML). With our activities in quantum-assisted optimization, we are exploring which methods are suitable under which conditions to solve
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and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods