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: Outstanding scientific programming skills (Python, PyTorch/JAX) with a proven track record of developing, debugging, and scaling complex RL pipelines or custom simulation environments. Clean public
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or energy systems. Experience with one or more process simulation and/or modelling environments (e.g. Aspen Plus/HYSYS, gPROMS, Modelica, Python/Matlab‑based frameworks) and an interest in working with
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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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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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models); Strong programming skills in Python and experience with deep learning frameworks (e.g., PyTorch); Experience with distributed systems and edge AI; Strong publication record in reputable
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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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programming skills (e.g. Python, Matlab). Ability to analyse spatial (GIS) and large operational datasets. Interest in interdisciplinary work involving computer science. Strong written and verbal English
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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