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Field
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communities • Multivariate statistical analysis of community and environmental datasets • Spatial analysis and georeferencing of ecological data using GIS • Development and implementation of species
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analysis and multi-omics data integration workflows using Python/R Integrate digital pathology data with omics data (e.g., MS-based proteomics, spatial proteomics) Develop and drive independent project ideas
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, statistics, and programming languages such as R, Matlab, or Python are highly desirable. Requirements: Candidates should have completed (or be completing) a doctoral degree in psychology or cognitive science
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Multivariate statistical analysis of community and environmental datasets Spatial analysis and georeferencing of ecological data using GIS Development and implementation of species distribution models
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 4 days ago
performing preclinical fMRI or fUSI studies, including data analysis, rodent handling, surgery and behavioral experiments. Strong programming skills (e.g., Matlab, Python) are required. Additional knowledge
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, bioinformatics, systems biology, computer science or related field Strong proficiency in Python and/ or R Experience in single cell omics analysis and multiomics data interpretation Abilitity to work independently
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(required) Quantitative skills in time‑series analysis, biogeochemical data processing, and uncertainty assessment (R, Matlab, Python, or similar) (Required) Field experience (desired) Excellent English
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
., using FEniCSx) Advanced knowledge of scientific programming, preferably in Python, including experience with implementing machine‑learning methods (e.g., PyTorch) Excellent spoken and written English, as
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Science, or a related field. Demonstrated experience in image or signal processing, instrumentation data pipelines, or similar. Strong programming skills in Python, R, or MATLAB. Experience with statistical
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part of change Conception of novel stochastic source coding techniques based on channel simulation Development of numerical Python code for evaluation Optimization and refinement of these techniques in