Sort by
Refine Your Search
-
Country
-
Employer
- NEW YORK UNIVERSITY ABU DHABI
- AALTO UNIVERSITY
- King's College London
- Stanford University
- Technical University of Munich
- The University of Arizona
- University of Lund
- University of North Carolina at Chapel Hill
- Utrecht University
- Yale University
- ;
- Aarhus University
- Eindhoven University of Technology (TU/e)
- Great Bay University
- Harvard University
- Indiana University
- Lehigh University
- MOHAMED BIN ZAYED UNIVERSITY OF ARTIFICIAL INTELLIGENCE
- Mohamed bin Zayed University of Artificial Intelligence
- Northeastern University
- Pennsylvania State University
- SUNY University at Buffalo
- Saarland University
- Stony Brook University
- Texas A&M University
- University of California
- University of California, Los Angeles
- University of Copenhagen
- University of Florida
- University of Minnesota
- University of South Carolina
- University of Utah
- 22 more »
- « less
-
Field
-
, psychology, economics, political science, social research, engineering, and the humanities; High-performance computing, GPU-enabled workflows, large-scale data processing, research storage, and technical
-
for career development Access to high-performance computational resources (with GPUs) A collaborative environment across research fields, including plant biology, quantitative genetics, and population genetics
-
/ coarse- grained approaches) Experience with enhanced sampling techniques; computational biophysics/chemistry Usage of high-performance computing clusters, preferably GPU-based computing Proficiency in
-
, or population genetics Deep learning for sequence, EHR, or imaging data High-performance and GPU computing environments Excellent candidates from adjacent quantitative fields are encouraged to apply. The Research
-
with AMD MI300A GPU+CPU. 2. Perform benchmarking studies to enhance scalability and achieve high node-level efficiency, surpassing existing AMR frameworks. 3. Contribute to communication optimizations
-
establish a research profile. Develop and execute innovative research projects. Develop, train, and evaluate modern machine-learning models on GPU/HPC infrastructure. Integrate AI methods with scientific
-
). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
-
University of California, Los Angeles | Los Angeles, California | United States | about 21 hours ago
Nextflow or Snakemake, version control such as Git, and reproducible computational environments is preferred. Familiarity with GPU-accelerated genomics, high-performance computing, or cloud-based analysis
-
large GPU clusters on cryoSTEM datasets in the multi-terabyte range. This position plays a pivotal role in supporting ongoing, high-impact research programs within our lab. The successful candidate will
-
foundation models, 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