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About the Opportunity SUMMARY The lab of Professor Albert-László Barabási is looking for Postdoctoral Research Associates in the area of network science, nutrition, biological networks, machine
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Classification Title: Postdoctoral Position – Neuroimaging - Developmental Network Neurobiology Laboratory Job Description: The University of Florida, Department of Pediatrics, Neuroimaging
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-throughput screening, and online/in situ characterization with active-learning and Bayesian-optimization pipelines to guide experiment selection Build agentic artificial intelligence (AI) workflows and FAIR
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: Ph.D. in Physics, Computer Science or closely related areas Preferred: Demonstrated strength in one or more of the following. - Quantum many-body numerics: tensor-network methods, variational Monte Carlo
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interdisciplinary research program. The project also provides excellent opportunities to develop expertise in AI for biodiversity science while building a strong publication record and collaborative network
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, comparative metabolomics, and MS/MS networking. Job Description: A postdoctoral Associate position in metabolomics and natural product chemistry is available as part of an NIH-funded project on nematode
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and technologies. Knowledge of AI/ML is important, but the focus will be on the high-fidelity simulation environments, software networking bridges, and physical data collection architectures
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20 Jul 2026 Job Information Organisation/Company Łukasiewicz Research Network - Krakow Institute of Technology Department HR Research Field Other Researcher Profile Leading Researcher (R4) Positions
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both theoretical progress and practical implementation in communication networks. Through these efforts, the Postdoc will generate impactful academic contributions that support the design of next
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Postdoctoral Position in Computational Humanities to Research the Macroevolution of European Literat
Max Planck Institute for Empirical Aesthetics (MPIEA) | Frankfurt am Main, Hessen | Germany | 3 months ago, representing these influences through dynamic networks and directed graphs, and creating effective visualizations of large-scale literary networks. Using these inferred connections and networks, we will tackle