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Deutsches Zentrum für Neurodegenerative Erkrankungen | Ulm, Baden W rttemberg | Germany | 29 days ago
the contribution of somatic (mosaic) mutations that may occur during development or aging to ALS. To this end, we are offering a PhD position to employ targeted deep sequencing or single cell-resolved detection
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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will
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in VR, deep learning, computer simulation, or psychophysics are another advantage. Specific RequirementsThe position is designed to encourage intersectoral and international collaboration and two
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(e.g., Computer Science, Software Engineering, Machine Learning, Artificial Intelligence, Computational Linguistics, or a related field) Strong skills in machine learning and deep learning Experience
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identification context, while promising for network-level monitoring, has been largely underexplored. To this end, the project will explore the application of the next generation of deep learning algorithms, e.g
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-efficiency trade-offs, using automated configuration to find Pareto-optimal designs under real deployment constraints. 2) Build the distributed learning loop. Develop the learning and update mechanisms
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position (technician) will focus on performing Raman/FTIR on retrieved samples. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data.
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analysis will focus on building sophisticated Deep Learning models, e.g., Long Short-Term Memory (LSTM) networks, to accurately model DPs over time and predict mood deterioration. The project will implement
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
close collaboration at all stages of a research project exploring applications of deep learning and artificial intelligence to dynamic portfolio optimization problems. This position offers the opportunity
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, willingness to learn, and the ability to think creatively about complex physical systems are just as important as specific technical expertise. This PhD project—High-Fidelity Simulations of Geological CO2