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Field
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on the theoretical foundations of (Machine) Learning. These positions offer the opportunity for postdoctoral research in an intellectually vibrant, inclusive, and welcoming department. Selected candidates
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critical role in advancing computational materials science by developing and applying first-principles and machine learning methods, with a focus on interatomic potential development and large-scale
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learning algorithms. Good computer programming skills in R/Matlab/PerlPython. Knowledge of basic molecular biology, genomics, and epigenetics. Experience in next-generation sequencing data and scRNA-seq data
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appointment) in a relevant field, such as geodynamics, seismology, geophysics, or computational science. Ability to sit for extended periods (e.g., 6–8 hours/day). Visual acuity to read computer screens and
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: Proficient with Python, R, and Linux/Unix systems. Strong skills in computer scripting and pipeline development. Experience in project management Experience in network analyses. Previous post-doctoral
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score derivation and validation, and other relevant analyses. Develops R or Python scripts for data analysis, statistical modeling, and machine learning techniques, ensuring reproducibility and efficiency
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host interactions through network analysis, machine learning. - Ability to map microbial genes to biochemical pathway analysis - Excellence in research, communication and collaboration skills, as
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magic, quantum circuits, quantum algorithms (3) Quantum many body computations, including sign problem theory, Quantum Monte Carlo method, DMRG, and machine learning Appointments of postdoctoral
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 5 hours ago
receive guidance from a mentor and will learn about conducting research analysis within OP. You will engage with current OP staff on policy analysis projects and assignments in one of the following focus
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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups