Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Harvard University
- National University of Singapore
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Simons Foundation;
- Singapore University of Technology & Design
- UNIVERSITY OF SURREY
- University of South-Eastern Norway
- University of Texas Rio Grande Valley
- Aarhus University
- CRANFIELD UNIVERSITY
- Center for Devices and Radiological Health (CDRH)
- City of Hope
- Dana-Farber Cancer Institute (DFCI)
- Florida Atlantic University
- Georgia Southern University
- Hong Kong Polytechnic University
- INESC TEC
- Imperial College London
- NTNU Norwegian University of Science and Technology
- Nanyang Technological University
- Oden Institute for Computational Engineering and Sciences
- SUNY University at Buffalo
- UCL;
- University of California
- University of Idaho
- University of Maryland, Baltimore
- University of Nottingham
- University of Nottingham;
- University of Oslo
- University of Waterloo
- Zintellect
- 21 more »
- « less
-
Field
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
computing environments and GPU computing. Proven experience in weather and climate models development and applications. Experience in machine learning, deep learning, or AI applications for atmospheric
-
across the wider Faculty of Engineering. We are keen to attract candidates with extensive experience in DEM. Experience in DEM of clays and of using YADE would both be distinct advantages. About you You
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
meningsfull jobb blant yrende studentliv og innovative forskningsmiljøer. Du får en arbeidsplass som er tett på de store samfunnsutfordringene – og samtidig nær løsningene på dem. Et arbeid vi gyver løs på med
-
the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
-
team and supported by cutting-edge HPC and GPU infrastructure, you will contribute to internationally leading research, publish in high-impact journals and present your work at major scientific
-
population and comparative genomics to examine genetic diversity, selection, pangenome relationships, and functional conservation. You will also develop reproducible GPU- and CPU-based high-performance
-
streams with perturbation signatures and fit these. For these fits, we will explore the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use
-
programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image