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investigator), Professor Andrei Popescu (co-investigator), and the wider COVERT team. Modern hardware architectures increasingly combine complex execution and memory technologies, including out-of-order and
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three universities / develop cutting-edge stochastic multiscale models for biology and ecology / supportive entry-level role with strong mentoring Join our multidisciplinary team to shape new mathematical
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(100%, E 13 TV-L, 3 years + possible extension, m/f/d) We seek a highly-motivated postdoctoral research fellow with strong expertise in cognitive computational modeling and an interest in applying
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10 Jul 2026 Job Information Organisation/Company Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH Research Field Technology Biological sciences Researcher Profile First Stage Researcher (R1) Positions Research Support Positions Application Deadline 20 Jul 2026 - 23:00...
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Center for Drug Evaluation and Research (CDER) | Southern Md Facility, Maryland | United States | about 23 hours ago
therapeutics and generic drugs. These efforts cover more than just medicines. Research Project: The overarching research project supports computational modeling efforts to address issues in polypharmacy and
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Is the Job related to staff position within a Research Infrastructure? No Offer Description We invite applications for a Postdoctoral Research Fellow position in remote sensing and modeling
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scientific knowledge, such as physical laws, differential equations, and domain-specific constraints, to model, simulate, and understand complex systems. The project will explore modern SciML methods
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organizations. Strong quantitative, analytical, and written communication skills, including proficiency with energy modeling software and large data sets. Ability to communicate complex technical and modeling
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the contract will be bound, as well as collaboration in the teaching and third mission activities of the Faculty. In particular, the project sits at the intersection of XAI (Explainable AI) and complex systems
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on open-set recognition, foundation models, multimodal approaches, and uncertainty-aware, incremental learning to build adaptable AI systems that recognise known species while handling unseen classes