图像重建软件工程师

Job Description Key Responsibilities • Feature and Algorithm Development: Implement MRI application feature, Design, implement, and maintain application services and APIs • Deep Learning Recon: Build and train reconstruction networks (UNet/VarNet/physics-guided, score-based) using PyTorch/TF • Performance Optimization: Improve performance, reliability, observability, and developer tooling, GPU/CUDA acceleration, multithreading, memory optimization for real-time/near–real-time recon • Data Pipeline: Handle k-space data, calibration, coil combination, ESPIRiT maps, bias correction, post-processing • Engineering Excellence: Write clean, testable code; unit/integration tests; CI/CD; containerization and deployment • Standards & Integration: Work with DICOM/ISMRMRD; integrate with Gadgetron/BART/internal platforms • Validation & Translation: Partner with MR physicists and clinicians for experiments, benchmarking, and productization • Collaborate on architecture, technical roadmaps, and cross-team initiatives • Participate in agile processes: planning, estimation, retros, and on-call (as needed)

Qualifications • MS or PhD in CS, EE, Biomedical Engineering, Medical Physics, Applied Math, or related field • Strong math and signal processing foundation (linear algebra, optimization, Fourier/sampling theory) • Proficient in modern C++ with solid engineering practices • Knowledge of MRI reconstruction fundamentals: k-space, sampling trajectories (Cartesian/Spiral/Radial), parallel imaging • Expertise in at least one of: • Optimization/Inverse Problems: ISTA/FISTA/ADMM/PGD • Parallel Imaging: SENSE/GRAPPA/ESPIRiT • Non-Cartesian Recon: NUFFT, Toeplitz/blocked-Toeplitz approaches • Deep Learning Recon: End-to-end or physics-constrained networks, Plug-and-Play • Comfortable with Linux, Git, debugging, and performance profiling Nice to Have • CUDA/CuFFT/CuBLAS, OpenMP, SIMD vectorization • Experience with git, jenkens, devops, etc. • Hands-on with real MR data and sequences (EPI, 3D GRE/SE, VIBE, • bSSFP, DWI/DTI, ASL, MRE) • DICOM/HL7, PACS/workstation integration, and medical software compliance (IEC 62304, ISO 13485) • Publications/competitions (e.g., fastMRI) or open-source contributions Soft Skills • Self-driven, outcome-oriented, turning research code into production • Strong communication and cross-disciplinary collaboration • Documentation and experiment reproducibility mindset

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