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Field
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engineering, control engineering, or a related discipline. A strong foundation in at least one of: electrochemical or energy-system modelling, dynamic modelling and control, mathematical optimization, or data
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develop AI tools for the interrogation of subcellular proteomics data. We are interested to hear from candidates with experience in probabilistic or statistical modelling of complex biological data, such as
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description In this project, we develop a new mathematical framework to efficiently model
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and sustainability; Investigate and apply artificial intelligence and machine learning techniques, including large language models (LLMs), across CENSE’s scientific body in its five thematic areas
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) project aims to develop very high-power batteries (full charges or discharges in a few minutes), as well as the models and control strategies that allow their safe operation. Twelve laboratories in Germany
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modelling. References/further reading Wang, X. Q. et al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature 621, 868-876 (2023). Danenberg, E. et al. Breast tumor
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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18 Sep 2026 Job Information Organisation/Company Silesian University of Technology Research Field Technology » Other Computer science » Other Mathematics Engineering Researcher Profile First Stage
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 1 hour ago
Intelligence and the Life Sciences. We are building an AI-native, closed-loop research engine where automation, computation, and intelligent agents work together to decode, model, and rewrite biological circuits
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time