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Field
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. The project will involve drone optimizations, computer vision, closed-loop control, multi-modal sensor fusion, machine learning, and transfer learning methods. The candidate will also work in collaboration with
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computer science, data science, statistics, bioinformatics, electrical engineering, computer engineering, biomedical engineering, mechanical engineering, operations research, robotics, computational neuroscience
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project milestones and contribute to technical reports and project updates. • Prepare manuscripts, presentations, and other scientific materials describing research methods and findings. • Prepare materials
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computing, with a particular emphasis on methods tailored to study nonequilibrium quantum many-body systems. The position offers an exciting opportunity to contribute to cutting-edge research
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to scientific computing and other scientific domains. Candidates with strong backgrounds in stochastic analysis, numerical analysis, sampling methods, optimization, scientific computing, or related areas
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should be motivated to develop AI methods and apply them to clinically or epidemiologically meaningful research questions. We are particularly interested in candidates with experience in longitudinal data
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) and contribute to LU-PCG’s research under the U.S. Department of Energy’s (DOE) GENESIS Mission. Research will involve developing fast neural surrogate models, state estimators/virtual sensors, multi
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of single-cell analysis methods to investigate quiescence at individual cell and population levels. Where to apply E-mail [email protected] Requirements Research FieldBiological
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methods for the modelling and causal analysis of mass transit systems. The research will focus in particular on the development and application of data science methods to address key challenges in capacity
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methods (e.g., variational autoencoders) applied to biomolecular dynamics data. ● Familiarity with elastic network modeling (ANM/GNM) or the ProDy software package. ● A track record of independent