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libraries (e.g. Python, TensorFlow/PyTorch, Scikit-learn) Demonstrated experience in applying machine learning or data-driven methods to physical systems, preferably in the context of fusion research Proven
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policy, economics, statistics, or a related quantitative field Additional Qualifications Strong skills in Stata, R and/or Python and experience analyzing complex data Demonstrated experience working with
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, Python, and SAS or Stata Demonstrated expertise in analysis of claims data Clear scientific writing and communication, an ability to work both independently and in teams, and a track record of publications
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demonstrated proficiency in programming, specifically in Python and R, as well as experience with modern deep learning frameworks like PyTorch or TensorFlow. In addition to technical skills, the candidate must
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and CHIP-seq. · Expertise in downstream analysis and biological interpretation of bioinformatic findings · Proficiency in R/Python programming, developing analysis pipelines and
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analyzing the subseasonal-to-seasonal variability of the climate system; Demonstrate experience in Python code development; and Knowlege of programming skills including Python code development. Preferred
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, sufficient programming fluency (e.g., Python; familiarity with common ML tooling) to run computational experiments, and excellent communication skills, including writing for publication and presenting results
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publishing high-quality research papers in top-tier conferences and journals. Proficiency in advanced programming and computational modeling tools (e.g., Python, distributed systems). Good written and oral
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highly skilled Postdoctoral Fellow with a proven dual‑mode research profile capable of independently performing laboratory experiments and coding predictive AI models in Python to forecast biomaterial
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quantum many-body theory with a focus on quantum impurity models (particularly Kondo model). Strong computational skills (with Python or Julia or C++ or Matlab or equivalent) and using numerical techniques