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background in thermodynamics, transport phenomena, and process modelling and computer programming with documented experience in developing and applying unit operation and flowsheet models for chemical
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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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communication skills Experience with all-atom and/or coarse-grained molecular simulations Programming skills and experience working in a Linux environment Candidates with backgrounds in: Physics Physical
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Postdoctoral position in biomolecular simulations of membranes and membrane proteins at SDU in Od...
coarse-grained molecular simulations Programming skills and experience working in a Linux environment. Candidates with backgrounds in: Physics Physical Chemistry Engineering are particularly encouraged to
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affinity to computational tools and methods, and strong programming skills (e.g., Python and/or R). Familiarity with research on emotions, social norms, incivility, or political violence. High level of
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Policies and their architectures - Advanced programming skills What we offer - a well-developed research infrastructure, laboratories and access to shared equipment - an exciting interdisciplinary
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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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regulation. Programming, scripting, or computational workflow development, including R or Python. Experience working across interdisciplinary projects involving analytical technology development and
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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