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-based and farm models with a focus on biogeochemical and hydrogeological fluxes. Knowledge of greenhouse gas inventories (methane, ammonia, nitrous oxide) Proficient skills with scripting (R, Python) and
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or related field. Expertise in graph machine learning and demonstrated experience in multi-omics data integration. Strong programming skills in Python and its scientific and graph ML libraries (numpy, pandas
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NextFlow is a plus. Programming experience is required (Python, R). Previous experience in Genomics data analysis Strong analytical, organizational, and record-keeping skills Interest in working in a
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operators, transformers/LLM) and NN training. Strong Python programming skills (as a plus: C++ or Julia) and knowledge of scientific computing libraries (numpy, scipy, JAX…) and machine learning libraries
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on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use. However, powerful as it is, MagTense is at present limited in its
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particular with Large Language Models. Solid programming expertise in Python. Be the main author in at least one journal publication in the area of AI4SE, published at a high impact journal. Experience with
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multidisciplinary team environment. Further, we will prefer candidates with some of the following qualifications: Solid background in programming using Python (PyTorch, TensorFlow), R or other languages. Experience
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multidisciplinary team environment. Further, we will prefer candidates with some of the following qualifications: Solid background in programming using Python (PyTorch, TensorFlow), R or other languages. Experience
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considered an asset in these positions. Applicants must also be able to demonstrate excellent ability to code with or learn computer programming languages, such as C++, C#, Python, and/or Matlab
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modelling, preferably WRF Experience of scientific programming and running code on HPC systems Experience with Fortran, Python and Linux Shell Any of the following is advantageous but not essential