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Performance Computing are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation, Diffusion models, Reinforcement Learning . The successful candidate
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-generation sequencing ( NGS ) data analysis, workflow automation, and cloud computing. This role will support research and production environments where reproducibility, scalability, and performance are
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in Linux/Unix-based high-performance computing ( HPC ) environments and job schedulers (e.g., SLURM or qsub); experience with cloud computing platforms (e.g., AWS , GCP , Terra) is a plus
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-using models, retrieval systems, or related AI systems Experience with large-scale datasets, distributed training, or high-performance computing environments Interest in scientific applications of AI
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. Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment
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performance and funding. Resources for research expenses including publication charges, related conference travel, workshops, and computing resources will be available. Further information about the BHI may
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Telescope project) and Michael Johnson (Astrophysicist, Harvard-Smithsonian CfA). Successful candidates will have access to our computing facilities and will have the opportunity to interact and work
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advantage of unparalleled computing resources in the academic environment by optimizing AI/ML models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new
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tools is highly desirable. ● Packaging and environment management (Conda/venv); containers (Docker/Singularity); CI/CD; build systems (CMake); version control (Git). ● High-Performance Computing
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-training, adaptation, or evaluation Experience with agentic workflows, tool use, retrieval systems, or related AI systems Experience with large-scale datasets, distributed training, or high-performance