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Field
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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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for seeking the position. Extensive experience with Python, Matlab, and R, and good UNIX knowledge are essential skills, as well as familiarity with biological omics data analysis techniques. Experience with
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Engineering, or a related discipline - Solid understanding of fluid dynamics and/or electromagnetism - Programming experience (preferably Python) - Interest in machine learning and scientific computing
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, python, C++ etc). Any experience or capabilities in engineering design or manufacturing methods would be advantageous. Eligibility and Application Due to funding restrictions, the position is only
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data (nationwide LiDAR coverage at 50 cm resolution). The candidate will perform quantitative morphometric analyses of landscapes and river networks near suspected active faults using GIS tools, Python
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interdisciplinary team with clinicians and engineers; You have strong programming skills in Python; You have knowledge of medical image processing, and machine learning and deep learning techniques; Written and
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Python, MATLAB, or R) is an asset.•You can conduct independent research (demonstrated, e.g., by an excellent Master’s thesis) as well as collaborate well in teams.•You have excellent command of spoken and
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capable of stereotaxic surgery and handling delicate recording probes. They should be strongly self-motivated, ambitious, and able to use Python and/or MATLAB to analyze neural and behavioral datasets
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automation, data science, python. The ability to collaborate in a multidisciplinary research environment is essential. Personal initiative, ability to work systematically, reliability, responsibility, teamwork
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*ideal candidate* is expected to have: - Very good experience in programming in Python or Matlab and data analysis (essential) - Research experience (essential, e.g., through research MPhil/Master’s degree