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reaction. Expertise on data analysis with R or Python tools. Experience in processing of targeted and untargeted mass spectrometry datasets, ideally in the field of lipidomics or other omics sciences
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support. Preferred Knowledge, Skills, and Abilities: Ability to program and debug python code, particularly Jupyter notebooks. Experience in working on collaborative software projects. Environmental, Health
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Engineering, Mathematics, Statistics, or related fields. • Strong programming skills in Python, Java, C++, etc. • A solid foundation in generative AI, machine learning, and related areas. • An Interest in eye
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some of the following areas: molecular dynamics, Monte Carlo simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic
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. Proficiency in computer programming, including scripting in Python, Fortran, or other computing tools for data processing and modeling. Experience in grant proposal writing within a collaborative environment
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, computer science, statistics, or a related field together with strong programming skills in Python, R, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale
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models (e.g., CNNs, diffusion models, etc) Proficiency in Python Experience with HPC (CPU or GPU, with GPUs preferred) Related Skills and Other Requirements Ability to collaborate on the application of AI
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Operations, Laboratory Techniques, Linux, MATLAB, MRI Analysis, Oral Communications, PET/CT, Python (Programming Language), Researching, Results Reporting, Scientific Writing, Statistical Analysis, Unix
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for research (e.g. Python, R and RStudio or MATLAB) and using data-driven pipelines with complex bio-behavioural data. Prior knowledge or keen interest in topics at the intersection of experimental psychology
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applications. • Strong programming skills (e.g., Python ) • Strong machine learning skills applicable to manufacturing processes Preferred Qualifications • Ability to perform experiments and data analysis