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evaluate usability of system architectures such as Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference - implementation of your machine learning pipeline in Python (using e.g
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sample collections Conduct and supervision of laboratory analyses, in particular immunological assays Data management and programming in R and Python Writing and contribution to original publications
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Approach ( https://vhrz669.hrz.uni-marburg.de/ssf/ ). Understanding the mechanisms controlling subsurface flow (SSF) and the conditions under which it occurs remains a major challenge in hydrology and
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experimental systems for cryogenic measurements Development of a microwave quantum control & readout stack Development of Python code to operate quantum systems Detailed experimental characterization
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for data acquisition and analysis (e.g., Python or LabVIEW) are highly desirable. After a training period, you will operate and maintain the fast EC-STM system and contribute your own ideas to the project
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. Requirements Masters degree in physics (grade of "good" or better). Experience in scientific programming (e. g., Python, Julia). Interest in experimental physics. Good command of English (that will be
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part of the Forschungsverbund Berlin (https://www.fv-berlin.de/ ) and the Leibniz Association (https://www.leibniz-gemeinschaft.de ). You can find more details on the institute webpage: https://www.ikz
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Previous experience in protein biochemistry, LC/MS based mass spectrometry Previous experience in handling omics data analysis using software Experience in R and python is a plus but not mandatory Good
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important - Prior experience with density functional theory or machine learning is desirable - Proficiency in the Python programming language is important, as well as Fortran - Strong written and oral
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familiar with data analysis using programming languages like R, and/or Python. You have excellent communication skills and a willingness to collaborate across disciplines. You fit to us: if you have strong