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Field
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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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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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, 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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in programming languages such as R, Python, or Julia. Excellent communication and collaborative skills. Strong publication records in peer-reviewed journals is an advantage. Experience with biological
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-on skills, with some project development or experimental experience, and the ability to participate in algorithm design, validation, and system integration; (d) proficiency in Python, C#, C++ or other
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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
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discipline from a strong institution. Skills: - Proficient in programming languages such as Python, with experience in using AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). - Familiar with high
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the context of technology sectors. He/she should have good programming/coding skills such as SQL, Python and/or Java and be competent with statistical packages like STATA or R (STATA preferred). Skill and