Sort by
Refine Your Search
-
distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross‑Paradigm Integration (new optional emphasis): Explore how
-
Temporary Job Status Full-time Offer Starting Date 1 Dec 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number BAP-2026-608 Is the Job related to
-
large-scale numerical modeling of semiconductor laser arrays at the College of Optics and Photonics (CREOL), University of Central Florida. Our research program investigates collective dynamics
-
, scalability, and efficient utilization of computational resources. Experience with GPU accelerators and computing ecosystems such as CUDA and/or AMD ROCm/HIP. Experience with performance profiling and
-
on modern accelerators. Sparse and constrained tensor decompositions — CP- or Tucker-type factorizations, constrained least-squares solvers, or scalable tensor kernels. GPU performance engineering — CUDA/HIP
-
to the development of the HashBrick library, an open source framework for adaptive mesh refinement, written in CUDA and supporting one-sided communication for scalability. The target architecture is a supercomputer
-
frameworks such as Qiskit, CUDA-Q, PennyLane, Cirq, or equivalent platforms, and knowledge of quantum algorithms, quantum error correction, fault-tolerant computing, or quantum resource estimation. Experience
-
distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross‑Paradigm Integration(new optional emphasis): Explore how
-
application programming models such as CUDA, HIP, SYCL, OpenMP, Kokkos or Raja, vectorization; MPI; and one-sided asynchronous programming models. Experience in heterogeneous computing, developing and debugging
-
transfer (CHT) or fluidic manifold optimization Experience with machine learning/AI (PyTorch or TensorFlow), reduced-order modeling (ROM), or data assimilation (DA) Experience with GPU programming (CUDA and