56 software-defined-network-postdoc Postdoctoral positions at Oak Ridge National Laboratory
Sort by
Refine Your Search
-
the laboratory. Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs. Special Requirements: Postdocs
-
-throughput phenotyping at ORNL's Advanced Plant Phenotyping Laboratory (APPL) with paired geochemical and microbial measurements. Utilize laboratory equipment and software that may include a Picarro gas
-
credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting
-
at scale. Experience in HPC and associated software development for applications, middleware, and/or system software. Flexibility to adapt to diverse R&D projects and tasks. Effective communicator in both
-
, quantify nonlinear interactions, and advance scientific understanding of coastal ecosystem resilience. Curate and develop reproducible AI-ready datasets, computational workflows, and research software
-
workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed
-
Requisition Id 16736 Overview: We are seeking a Postdoctoral Research Associate who will work with a multi-disciplinary group of experts in economics, engineering, software development, and building
-
modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
-
/or machining process optimization Hands-on experience with CNC machine tools, machine controllers, G-code, CAM software, machining process planning, fixture design, and practical manufacturing
-
that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and