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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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skills and strong publication records to apply. Some skills in analyzing/evaluating large data sets are highly desirable. Knowledge of an advanced programming language such as MATLAB, Python, etc. is
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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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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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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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consortium connects AI model development directly to large-scale microbiome data generation, microbial culture collections, greenhouse experiments and field validation. More information For more information
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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-associated virus (AAV)-based gene therapy treatments for inherited retinal disease (IRD). The successful candidate will generate translational research data required to support the preclinical development
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matter (or other “invisible” new physics signals) at the Large Hadron Collider (LHC) compare high-dimensional collision data against detailed simulations whose likelihood cannot be evaluated, only sampled
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statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data). Background in health informatics, population health research