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networks, online analysis with delay, and theory of distributed algorithms. Job description In our group, we try to apply and unite the approaches and techniques of theory and practice. Some members of our
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. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
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challenge. The ESA-funded LISA will rely on an extensive ground segment for data processing and analysis. The LISA ground segment is a distributed facility (the LISA Distributed Data Processing Center
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, you will design, prototype, and optimize advanced simulation algorithms—particularly in the domain of cloth and deformable materials and contribute to our next generation of rendering and learning-based
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) distributional generalization, transfer learning, causality Multi-objective settings and alignment, RL theory Statistical learning theory, optimization (e.g., implicit bias) Robustness (broadly defined), privacy
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development of algorithms and large-scale numerical simulations. Your expertise will extend to various areas, including quantum Monte Carlo, machine learning, quantum computing, quantum machine learning, and
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the theoretical and algorithmic foundations of AI. A strong commitment to excellence in undergraduate and graduate teaching and mentorship is essential. Preference will be given to candidates who show promise in
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and dynamics, which we also plan to investigate using AI-based pattern recognition algorithms. In this project, the PhD student will: Run the MIT General Circulation Model (MITgcm) together
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methods that can adapt to changing external conditions and continuously learn from data in a distributed way. Coupling of new theory with effective implementation strategies have the potential to make a
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continuously learn from data in a distributed way. Coupling of new theory with effective implementation strategies have the potential to make a lasting impact on building efficiency through sustainable