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ESSENCE: Efficient Self-Supervised Machine Learning for Adaptive Wireless Communication Systems This project investigates self-supervised learning (SSL) for wireless communication systems to improve
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stimulation devices in collaboration with engineering and clinical teams. Data Collection & Analysis Acquire, process, and analyze high-dimensional electrophysiology and behavioral datasets. Use tools such as
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between behavioral and computer scientists. The ideal candidate has some knowledge in both areas, and the specific behavioral domain is open to discussion. Project B – Understanding and Countering
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23 Oct 2025 Job Information Organisation/Company Adam Mickiewicz University, Poznań Research Field Chemistry » Other Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Country
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the next generation of secure agentic AI systems through cutting-edge research in adversarial machine learning and formal verification. The Role As a research scientist, you will contribute to frontier AI
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 30 days ago
the machine learning community as challenging, high-dimensional testbeds. Notably, the recently developed WOFOSTGym simulator \cite{solow2025wofostgym}, bridging crop modeling and RL, received the Outstanding
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polar orbit, passing near the poles about 15 times per day and regularly observing the CIFAR study region. Its payload - two optical cameras, a thermal camera, and onboard machine-learning capabilities
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning for improving
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive
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models that include these mechanisms. The postdoc will develop biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM