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: Molecular modelling and simulation experience during PhD Ability to present complex information effectively to a range of audiences Experience of manipulating and visualizing large datasets We value
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1 Sep 2026 Job Information Organisation/Company Charité – Universitätsmedizin Berlin Research Field Computer science Researcher Profile Recognised Researcher (R2) Application Deadline 11 Sep 2026
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framework for the joint analysis of large-scale structure (LSS) and gamma-ray data. By combining these complementary probes of the same underlying matter distribution, the project aims to sharpen constraints
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The successful candidate will lead research in one or more areas such as: Large-scale Internet and Web measurements. LEO Satellites. Internet access and digital inequality in developing and underserved regions
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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are particularly relevant. The developed numerical models will leverage a large suite of in-situ and remotely sensed observational datasets, spanning from subsurface to atmosphere. You will have experience
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public health. In this role, you will develop and evaluate novel AI and machine learning methods using large-scale multimodal datasets, contributing to epidemiology-informed foundation models, predictive
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based on fossils. This will make it possible to pinpoint cognitive changes in geological time, which can be correlated with major events in the history of Earth. More information on our team can be found
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extreme weather forecasting, employing data science models as developed in computer vision and natural language processing (NLP). This project leverages the large-scale high-performance computing (HPC
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systems routinely handle data rates of several GB/s and support large-scale scientific data workflows. To support sustainable data management at next-generation photon science facilities, we are seeking a