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species distribution models, joint/hierarchical community models, dynamic vegetation models, or related biodiversity forecasting approaches; advanced programming skills in R and/or Python and
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Experience with programming in Python, R, or similar languages Background in machine learning or deep learning methods Knowledge of genomics, transcriptomics, evolutionary biology, and plant biology is an
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level, which is in Danish). Scientific computing, possibly with different programming languages (e.g., Python, MATLAB/Simulink). Moreover, the candidate shall demonstrate the following experiences
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programming in Python, R, or similar languages Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to
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computational tools (e.g., web technologies, Python libraries, client/server systems). Excellent communication and interpersonal skills with the ability to work effectively in a collaborative research
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quantitative data analysis and scientific programming, preferably in Python is required. Experience with hyperspectral imaging, atmospheric spectroscopy, greenhouse-gas measurements, UAV observations, radiative
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, dynamic vegetation models, or related biodiversity forecasting approaches; advanced programming skills in R and/or Python and experience with reproducible analytical workflows; experience handling large
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, systems integration). Perception and machine learning for robotics. Software design and development (C, C++, Python), including real-time and embedded systems. UAS operations (experiments, piloting
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. Metagenomics and/or metabarcoding. Bioinformatics and analysis of high-throughput sequencing data. Statistical analysis using R and/or Python. Reproducible computational workflows. Scientific writing and
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. Strong skills in scientific scripting (R, Python) and programming. Experience with High-Performance Computing (HPC) environments and the management/analysis of large datasets. The ability to