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powerful, modern analytical techniques including chemical proteomics and metabolomics. They will have access to advanced synthesis facilities, as well as biological models, such as macrophages and organoids
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will be trained in a variety of powerful, modern analytical techniques including chemical proteomics and metabolomics. They will have access to advanced synthesis facilities, as well as biological models
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, energy-related datasets. Proficiency in Python, MATLAB, and/or Julia for modeling, simulation, and data analysis. Familiarity with GIS tools (e.g. QGIS), time-series databases (e.g. InfluxDB), and version
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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
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aims at developing tools using large language models (LLMs) for the correction of misinformation about climate change in social media. The successful candidate will develop innovative tools leveraging
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on addressing the complexity of the device-software-application-data design space, enabling systematic and efficient exploration using modeling and simulation tools.## Key Responsibilities- Identify and
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, transcriptomics, and proteomics and work on advanced biological systems for modelling infection including macrophages, organoids, zebrafish, primary and clinical samples. Embedding within the Institutes of Biology
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for Theoretical Physics at Utrecht University invites applications for a PhD position in cosmology with galaxy shapes, supervised by Dr Elisa Chisari. The project combines theoretical and simulated studies
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data