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Field
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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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. Research directions. Successful candidates will lead one or more of the following ongoing projects: • Developing neural differential equation and continuous-time dynamical models for spatial and single-cell
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funded through the ERC project SleepCog and associated grants, with an appointment of two years. Start date is January 2027 or by agreement. About the Group The Boccara group investigates the neural
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Requirements: The prospective candidate should be well-versed with deep neural networks, have experience working on PyTorch or similar DL frameworks, programming in Python (preferred), NLP packages and pipelines
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relevant technical software, such as Cadence, SPICE, Verilog, etc. are needed; • Background knowledge in neural network algorithms preferred, but not required • Collaborative skills, student mentorship
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methods with optimization and decision-support models. Background in one or more of the following: time-series analysis, neural networks, forecasting, uncertainty quantification, sensitivity analysis
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
generative models and/or graph neural networks. Domain knowledge of Mechanical Engineering problems is not required. The ability to transfer your knowledge to Engineering problems is however expected. Please
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such as classifier free guided diffusion models, transformers with multi-headed attention, physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression
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of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes that focus
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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian