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Field
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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treatments for mental illness. To this end, we bridge computational models that target various levels of analysis, including the algorithms (e.g., reinforcement learning models) and their neural
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machine learning Data analysis and advanced statistics Economic and social transformations related to digitization Experince when it comes to programming (preferably Phyton) and in the use of modern tools
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market using data available on online recruiting platforms, deploying state-of-the-art approaches in Natural Language Processing, Semantic Web, and Agent-based Modeling. For this purpose, an extensive
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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: Modelling of optimization problems mainly related to the indicated line of research, as well as the design, implementation, and validation of algorithms to solve them Where to apply Website https
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simulations, optimisation, machine learning and turbulence modeling. The researcher must hold a Phd in fluid mechanics / Applied mathematic / Machine Learning. Website for additional job details https
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performance guarantees. Experience with motion planning, stabilization, and obstacle avoidance algorithms in dynamic or uncertain environments. Familiarity with learning-based methods (e.g., machine learning
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and a heterogeneous emerging computer architecture, collaborate regarding compiler and other tools as well as modeling their hardware for integration into the emerging computer architecture framework
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for experiments using reinforcement learning, Bayesian methods, image analysis and data analysis. Collaborate with interdisciplinary teams, including machine learning experts, device modelling specialist