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Field
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data analysis capability, including experience with statistical software such as R or Python. Soft Skills: Strong academic writing, teamwork, and communication skills. Track Record: Evidence of academic
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Multiphysics, Abaqus), CAD software (Solidworks, AutoCAD), Visual C++, LabVIEW, Pythons, EEG, and eye-tracking systems Demonstrated expertise in the area of design, methods, modelling, and optimization
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or coding platforms including SketchUp, EnergyPlus, OpenStudio, TRNSYS, and Python; Proven ability to work at a fast pace and to react rapidly to new information; F1 experience would be advantageous but not
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systems (Mojo, Julia, Rust, Python), and HPC system co‑design. This position is embedded within the larger DOE ASCR ecosystem, with direct relevance to ongoing efforts, and related AI‑for‑HPC thrusts
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systems Experience in large-scale data processing, signal analysis or real-world system deployment Proficiency in programming (e.g., Python) and ML frameworks (e.g., PyTorch, TensorFlow) Experience with
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the development of data-driven insights and scholarly publications Position Requirements: Technical Proficiency: Strong command of Python or R for data manipulation (e.g., Pandas, Tidyverse) and SQL for database
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ruminant nutrition • Experience with quantitative or mechanistic modeling • Ability and willingness to work in Python-based environments Before applying, please note that to work
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of genome biology, bioinformatics and/or machine learning. Experience with python/R programing, machine learning theory and tools. Preferred Qualifications Publication record with minimum 2 years experiences
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extensive experience of python programming, deep neural network analysis, and large language models. Ability to work independently and generate innovative, out-of-the-box research ideas. Ability to work
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Python and experience with GPU cluster environments (e.g., SLURM) are a plus. Special Instructions Please provide a CV, a Research Statement, and two or more letters of recommendation. The target start