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Field
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machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints
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, diverse group Experience working with standard computer software Previous experience working with and caring for rodents Excellent technical skills for bench work including gene expression analysis, immune
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analysis including econometrics, statistics and machine learning and related disciplines handling large amounts of complex data. They should provide evidence of potential for research and publication
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15 Jul 2026 Job Information Organisation/Company Humboldt-Universität zu Berlin Department Physics Research Field Physics » Other Researcher Profile Recognised Researcher (R2) Positions PhD
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many areas of applied and theoretical statistics and data science, and is heavily involved in research at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to
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, including but not limited to deep learning, computer vision, computational linguistics, pretraining methods, interpretability, and transfer learning Experience with cognitive science, particularly
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pension 29 days annual leave plus bank holidays, along with Christmas closure Ride to work and EV car scheme available For more information, please see University of Manchester Benefits . You can also find
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application of machine learning and AI methods to large-scale, longitudinal, routinely collected eRegistry data. The successful candidate will collaborate with researchers, PhD candidates, postdoctoral fellows
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computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in Python and deep learning frameworks (e.g., PyTorch) Interest in applied, industry
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs