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Field
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computational methods and tools, including prior experience with algorithms relevant to computational biology, is a plus. ● Ability to work independently as well as part of an interdisciplinary team in a
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
, scientific computation, scientific software and algorithm development, and data analysis; demonstrated ability to conduct original, high-quality research in computational fluid dynamics and/or computational
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will also investigate process optimisation algorithms, providing a route toward autonomous optimisation and ultimately enabling long term stable operation under variable load conditions. This post is
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models, and efficient algorithms for large-scale genomic data analysis. This is a one-year appointment starting as early as possible, with renewal possible based on the availability of funds, availability
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-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs. The successful
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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-powered diagnostic algorithms, and integration with digital health platforms for real-time monitoring and clinical decision support. WITEC brings together interdisciplinary researchers from MIT and
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). Experience in the application and development of computational methods/tools or machine learning algorithms. Good computer programming skills in R/Matlab/PerlPython. Knowledge of basic molecular biology
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deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical optimality with computational efficiency. Reinforcement Learning through Stochastic Control. We will