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care for patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 13 days ago
acquisition Good communication skills in English and ability to collaborate in interdisciplinary teams Desirable qualifications Experience with machine learning methods for regression or signal interpretation
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or PhD in public health, epidemiology, statistics, biostatistics, math, economics, or quantitative social sciences plus two years’ experience preferred. Experience with machine learning, data mining, and
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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explicit model of the biophysical effect of land use change, a machine learning emulation of dynamic global vegetation models. Both activities aim to improve understanding and quantification of the effects
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model organisms in their work and are pushing into emerging model and non-model organisms that are proving uniquely valuable in particular studies. To learn more about our department, https
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, earth or energy. Learn more at www.hds-lee.de . Institute specific promise here. We are looking to recruit a PhD position – Co-regulation structures for large-scale single-cell transcriptomics – within
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» Autonomic computing Engineering » Maritime engineering Technology » Computer technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 25 Apr 2026 - 23:59 (Europe
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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision
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the application of these methods to problems in the physics of oxides, semiconductors, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists