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, maintenance, and data analysis. Strong background in MS-based proteomics data analysis and familiarity with modern computational tools. Additional experience in high-throughput proteomics, low-input proteomics
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-08187-1 Your Profile: Master and PhD in biology, genomics or bioinformatics Strong background in data science or machine learning (deep learning, statistical modeling, or large-scale data analysis a plus
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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | about 2 months ago
in acquisition techniques and deploying novel analysis pipelines and approaches. The ideal candidate (m/f/d) must have a strong background in MRI data analysis and neuroimaging methods, and hold a PhD
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reporting skills (4) Experience in spatial data analysis using geographic information systems (GIS) and programming languages (R, Python) as well as experience in numerical model applications and multivariate
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research studies for automated image analysis. In particular, you will: Plan, develop, and implement AI/ML algorithms for pathology image analysis. Integrate multi-modal data (e.g., genomics, clinical data
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its detailed analysis through Oxford Nanopore Technologies (ONT). Your role will be central in creating and applying bioinformatics and machine learning tools to analyze long-read data and decipher cap
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Postdoc (f/m/d) Methods for Geochemical Exploration and Sampling Strategy / Completed PhD in the ...
mainly surficial geochemistry, e. g. soil and plant sampling, but also knowledge from drill core assets and geophysical exploration data # Analysis of the efficiency of the sampling strategy based
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journal articles Contribution to the overall support of the project, working group, and team collaboration Requirements: PhD in data science, or in any field with relevant experience Experience with
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will include: You will be involved in data analysis in close collaboration with international and national partners You will analyze and integrate various types of generated data (e.g., next-generation
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to develop an autonomous platform for organoid culturing and analysis to address key challenges in organoid-based experiments. This interdisciplinary project will leverage robotic systems for high-throughput