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) Neuromodulation approaches (TMS, tDCS, TUS) Neurogenetics Computational modelling (machine learning, reinforcement learning) Our research bridges scales (local circuits to global networks) and species (humans, mice
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of separating fire-induced signatures from natural environmental variability (weather, canopy changes, tree motion) and fluctuations in the SoO sources themselves. Machine-learning methods will help improve long
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Python Arduino and C++ (Physical Computing) Creation of interactive objects and components Machine Learning and Natural Language Processing Specific Requirements Candidates must hold a PhD in engineering
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by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate
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, supply chain systems, and transportation systems. We also currently host methodological research in data analytics, machine learning, human systems engineering, optimization, simulation, and stochastic
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, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is
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(iii) complex architectures with tightly coupled components hinder modular adaptation. To address these limitations, we research a physics-guided machine learning framework that integrates physical
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30 Dec 2025 Job Information Organisation/Company AMBER laboratory Research Field Engineering » Electrical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country
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public. • Ability to communicate effectively across cultural boundaries and work harmoniously with diverse groups. • Demonstrated ability to effectively teach electrical, robotics, or computer engineering