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 Oslo
- Aarhus University
- CRANFIELD UNIVERSITY
- Center for Devices and Radiological Health (CDRH)
- 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 Texas Rio Grande Valley
- University of Waterloo
- Zintellect
- 17 more »
- « less
-
Field
-
computational resources (with GPUs) A collaborative environment across research fields, including plant biology, quantitative genetics, and population genetics Opportunities for collaboration and research visits
-
Interest & knowledge about oncology & immunology, molecular & cellular mechanisms Work with ML + generative AI + GPUs at scale Experience with clinical or biomedical data and workflows Enjoy multimodal
-
., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with team members
-
). Resources: Access to a project-dedicated H200 GPU cluster, travel funding for top-tier conferences, and the broader compute resources of the Vector Institute/Digital Research Alliance of Canada. How to Apply
-
optimization Familiarity with continuous-variable/bosonic systems, non-Gaussian states, multimode entanglement, or Wigner-function methods Experience with HPC workflows (cluster computing, GPU computing
-
/bosonic systems, non-Gaussian states, multimode entanglement, or Wigner-function methods Experience with HPC workflows (cluster computing, GPU computing, reproducible pipelines) Track record
-
scientific software development. Proficiency in C/C++ and Python, with experience in HPC environments (e.g., MPI/OpenMP; GPU experience a plus). Record of peer-reviewed publications appropriate to career stage
-
Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data streams • Background in distributed systems or edge
-
Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 6 hours ago
approaches for automated medical devices (e.g., physiologic closed-loop controlled devices). Developing multi-spectral computational modeling tools using GPU-based processors to map light propagation