Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing
-
MOHAMED BIN ZAYED UNIVERSITY OF ARTIFICIAL INTELLIGENCE | Dhabi Kalan, Haryana | India | about 1 month ago
, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM datasets in the
-
group working on the LHCb experiment at the Large Hadron Collider. The successful candidate will focus on developing LHCb’s real-time data analysis systems, including the GPU -based software
-
researcher Are you excited to help find and assess the most promising habitable exoplanets around solar-like magnetically active stars? Join us to build next‑generation GPU‑accelerated models of stellar
-
organize and analyze biomedical and healthcare data to promote health for all. More information about BIDS can be found here: https://medicine.yale.edu/biomedical-informatics-data-science/ (Link is
-
-equilibrium field theory, semi-classical methods in quantum many body dynamics, tensor networks and GPU-accelerated quantum evolution. Our work is concept- rather than method-centric. Candidates with
-
with parallel multi-GPU on the Koa HPC; creating Bush scripts to standardize SLURM jobs; developing systems involving YOLOv8 classification, generating large data sets using Roboflow, Python scripts, and
-
network of collaborators and strong access to compute through national GPU systems (NAISS, e.g. Berzelius and Arrhenius) and local GPU infrastructure. Project description The position offers significant
-
promising habitable exoplanets around solar-like magnetically active stars? Join us to build next‑generation GPU‑accelerated models of stellar dynamos and connect them to exoplanet discovery and space
-
Position Description An Associate in Research position is available in the Interpretable Machine Learning Lab ( https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in