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well as experience in omics data analysis, and possesses solid English-language skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX
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optimization techniques. You have experience with modern Deep Learning Frameworks (PyTorch, Tensorflow, Jax) and proven ability of CUDA and Python programming. Knowledge of, or prior experience with, optimizing
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, and computational models. This PhD position is centered on addressing these challenges through innovative computational methods, combining optical system design, signal processing, machine learning, and
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microbiology. They learn contemporary microbiological, molecular genetic, bioinformatic, biochemical, analytical, and biophysical methods for studying microorganisms. Key research areas include physiological
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PhD student position on development of innovative bifunctional oxygen electrodes for SOC technology.
development of innovative SOC stacks gathering deep knowledge on electrochemical and structural characterization of energy technologies such as fuel cells and electrolyzers. Among the characterization
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., Tacchetti, A., Bakker, M.A. et al. Scaffolding Cooperation in Human Groups with Deep Reinforcement Learning. Nat Hum Behav 7, 1787–1796 (2023). [22] Melnyk I., Mroueh Y ., Belgodere B., Rigotti M., Nitsure A
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interested in using AI to unravel the mysteries of the brain? Do you want to perform cutting-edge NeuroAI research and leverage deep learning to understand human vision? Then check out the vacancy below and
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, you will work on a cutting-edge, multidisciplinary research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural
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Informatics, Research Group Research-Development-Innovation is looking for a PhD-student with a doctoral grant. We invite applicants for a fully funded PhD position in machine learning, AI, and data science for
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. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and reinforcement learning approaches that adapt lineup presentation in real time based