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Field
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computational tools and algorithms developed in the lab for processing and visualizing proteomics data (known as FragPipe computational platform). The individual will work in close collaboration with other
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. Strong applied knowledge of classical machine learning algorithms, including implementation with scikit-learn, model evaluation using metrics such as accuracy, precision, recall, F1-score, and
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algorithms and data structures. Experience with AI frameworks and libraries (e.g., TensorFlow, PyTorch). Ability to develop and implement AI models for business applications. Knowledge of cloud computing
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reconstruction algorithms, and validation in different human diseases. Responsibilities: Design, implement, and evaluate novel MRSI and MRI pulse sequences for research and clinical translation. Develop Extraction
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. Collaborate with JGI scientists and researchers across Berkeley Lab, including the Computational Research Division, to evaluate and implement new methods and algorithms that enhance system capabilities and
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analysis, and algorithms of computational research questions at a level sufficient to converse with Princeton's world-class researchers to support the ongoing work. This will consist of independent research
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students in research projects and teaching courses in areas such as linear algebra, computational methods, probability and statistics, data science, algorithms, artificial intelligence, operations research
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to develop advanced algorithms, parallel computing capabilities, and software engineering of Green’s function based multiple-scattering code for computing the electronic structure and energetics
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data and analyzes outcomes. Performs research on current and future HPC, data, and CI technologies, hardware and software. Works on algorithm development, optimization, programming, computer
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Susquehanna International Group, Quantitative Research Internship ID: SIG -QSTINT28 [#26690] Internship Title: Machine Learning Internship Program – PhD Hire – NY/Philadelphia area Internship Type