Sort by
Refine Your Search
-
Country
-
Employer
- Nature Careers
- Argonne
- Chalmers University of Technology
- King Abdullah University of Science and Technology
- Technical University of Munich
- University of Groningen
- University of Minnesota
- Aston University
- Brookhaven Lab
- Carnegie Mellon University
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- NEW YORK UNIVERSITY ABU DHABI
- Oak Ridge National Laboratory
- SUNY University at Buffalo
- Stanford University
- Sun Yat-Sen University
- Technical University of Denmark
- The University of Memphis
- University of Antwerp
- University of Houston Central Campus
- University of Kansas
- 11 more »
- « less
-
Field
-
and prototype imaging system design Excellent programming skills, Medipix/Timepix detectors, analytical models, forward, and inverse problems, and prototype system development for clinical translation
-
power (CSP) systems optimization, computational heat transfer and radiative transport using sophisticated numerical modeling and machine learning approaches for forward and inverse problems in radiation
-
on regional to global scales through inverse analyses. She/He will also work on other issues related to global atmospheric chemistry, air quality, and model development. Responsibilities include initiating and
-
R240260 Posting Link https://www.ubjobs.buffalo.edu/postings/54003 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.89 Posting Detail Information
-
at the Computational Science Initiative (CSI), within the Brookhaven National Laboratory. The selected candidate will collaborate on solving inverse problem, relevant for interference lithography process, by deploying
-
Organisation Job description The advent of modern machine learning (ML) methodology is accelerating scientific progress by streamlining research across disciplines. In chemistry, it enables
-
containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling
-
of linear algebra and numerical optimization Understanding of statistical modeling and inverse problems is desirable Experience with programming languages like Python, MATLAB, or C++ Joy in dealing with
-
focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning
-
: Computational geoscientific skills in using geophysical and geological data for complex geological structure modeling. The research also requires skills in solving geophysical inverse problems. Demonstrated