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machine learning, computer vision, multimodal perception, probabilistic modeling, and interpretable artificial intelligence. The research will be conducted in collaboration with leading national and
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Postdoctoral Research Fellows in Proteomics: Neurodevelopment, Cancer and Drug Toxicity, 3 positions
characterize target engagement, selectivity and phenotype mechanisms directly in primary human cells and employ machine learning methods for data analysis. This includes activity-based profiling, global
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interpretation of host stars’ dynamo mechanisms and magnetic activity using fully compressible MHD in global and local frameworks, integrated with physics‑informed machine learning and coronal/wind
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contextual and behavioral cues to understand human actions, anticipate future behavior, and reason under uncertainty. The methodological scope includes machine learning, computer vision, multimodal
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Postdoctoral Research Fellows in Proteomics: Neurodevelopment, Cancer and Drug Toxicity, 3 positions
engagement, selectivity and phenotype mechanisms directly in primary human cells and employ machine learning methods for data analysis. This includes activity-based profiling, global- and
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activity using fully compressible MHD in global and local frameworks, integrated with physics‑informed machine learning and coronal/wind modelling. Key tasks and responsibilities: Develop and implement
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Official Statistics / Head of Unit at Destatis Munich (City), Bavaria (State) and Wiesbaden (City), Hesse (State), Germany Research Fellow in Computational Statistics and Machine Learning for Complex Systems Malaysia
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cells and employ machine learning methods for data analysis. This includes activity-based profiling, global- and phosphoproteomics and phenotype deconvolution for drugs causing liver toxicity. The project
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code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing. Your Profile The candidate must have
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interdisciplinary cohort of doctoral researchers and benefit from ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists