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, or measurement systems will be advantageous. Familiarity with signal processing and analysis, control systems, Python, MATLAB, C++, LabVIEW, or equivalent tools is preferred. Strong analytical, problem-solving
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, Bioconductor, tidyverse, SingleCellExperiment, or related software. Experience with Python and machine learning approaches is considered an asset. APPLICATION PROCEDURE Applicants should submit: Cover letter
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the master's degree has been awarded Solid background in mathematics and physics is a requirement Experience in scientific programming (e.g., Matlab, Python) is requirement Experience in statistical analysis
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analysis and programming (e.g. Python) Proven track record of successful publications Very good oral and written English skills Team player Additional Information Benefits We offer Our institution is based
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manuscripts and/or revisions) to the top OR journal listed above. • Proficiency in Python, particularly in optimization package usage and data-analytic related tasks. • Proficiency in scientific writing
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, population dynamics, or multicellular tissues. · Experience analyzing complex biological datasets using R, MATLAB, Python, or a comparable programming environment. · Experience integrating
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, Computer Science, Neuroscience, or a related field Strong experience in MRI data analysis (functional, diffusion, structural MRI) Proficiency in Python and/or MATLAB Experience with neuroimaging toolboxes (e.g., FSL
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and ML packages with a Python programming environment. Ability to troubleshoot complex biochemical assays, identify causes of non-specific amplification and design appropriate controls. Ability to plan
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languages (python, JAVA script, C++) & deep learning Technical expertise (e.g., adapting/tweaking/debugging code) Ability to create online experiments, using online platforms such as Qualtrics Contribute
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skills in English Candidates without a master’s degree have until 30.11.2026 to complete the final exam. Desired qualifications: Programming experience, e.g. in Python, particularly for machine learning