Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
the cosmological information contained in the non-linear structure of matter fields and weak gravitational shear, beyond traditional statistics, by linking cosmological parameters to observables via physical models
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
the structure of matter, from subatomic particles to the large-scale structure of the universe. Our departmental instructional mission spans all segments of the student community. Over 60% of all college
-
Science Foundation's Programmable Cloud Laboratories (PCL) Test Bed program to establish ELECTRA, a remotely accessible, AI-enabled automated laboratory for electron diffraction (MicroED) and structural
-
architectures), determination of optimal embeddings and encodings for protein structures, multiple alignment methods, Bayesian dendrogram reconstructions, and benchmarking including jackknife resampling. Position
-
to integrate heterogeneous molecular data, but are often less explicit about biological directionality and causal inference. This project instead builds on the structure of the central dogma, using genetic
-
Professor Harri Lähdesmäki. The position offers a broad local research network in Bayesian machine learning and computational biology. Your network and team Dr Martinelli is an independent Research Fellow
-
. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university’s broader compensation structure. We are committed
-
of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim or http://github.com
-
construction (you must not be able to wire authentication around the Auth Resolver); what happens to the language when gear contracts change or a 201st gear is added; projectional representation of graphs with
-
. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning