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Field
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) Item 03: Proven experience in both programming (Python, R, etc.) and the use of data analysis tools, as well as artificial intelligence / machine learning techniques (punctuation: 22.5) Item 04
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microenvironment structures are associated with genomic features and clinical outcome. Nat Genet 54, 660-669 (2022). Ali, H. R. et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic
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, particularly EEG; • Programming experience, particularly in R and Python; • Proficiency in statistical methods commonly used in psychology and neuroscience (ANOVA, linear models, etc.); • Familiarity with
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bioinformatics, computational biology, genomics, evolutionary biology or a related discipline. Essential skills include experience with genomic data analysis, proficiency in Python and/or R, familiarity with
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Development Incentives system (SIID), regulated by Ordinance No. 328 B/2023 of October 30, in its current version and under the terms of Notice MPr-2023-7 - SIID-R&D Empresarial - Co-promotion Operations
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Max Planck Institute for Heart and Lung Research, Bad Nauheim | Bad Nauheim, Hessen | Germany | 13 days ago
research experience in RNA biology and/or high-throughput sequencing technologies would be a strong advantage Familiarity with programming languages such as R or Python for data analysis is beneficial
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discipline. Desirable Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable. Knowledge of hydrodynamic
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Requirements Experience with laboratory work, environmental monitoring and/or data analysis will be considered an advantage. Programming and data-analysis skills (e.g., Wavemtrics, R, Python or similar
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and need treatment escalation. Key References: [1] H. Yu, S. Saffaran, A. Ali, C. Henry, N. Mustfa, A. Thomas, A. Rajhan, S. Isrhad, L. Weaver, R. Tonelli, L.S. Menga, Q. Zhang, M. Einollahzadeh Samadi
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ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy