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onwards: Scientific Researcher (m/f/d) E13 TV-L - Novel mathematical approaches for inverse modeling to determine greenhouse gas emissions. The position is initially limited to two years with
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The High Performance Computing department conducts research in the field of seismic data processing and interpretation. Employees from the fields of geophysics, physics, mathematics and computer
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scientific publications Your qualification profile Successfully completed university degree (master, diploma, or equivalent) in computer science, physics, engineering, mathematics, or comparable field Profound
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Leibniz-Institut für Agrartechnik und Bioökonomie e.V. | Potsdam, Brandenburg | Germany | about 1 month ago
completed university degree (master, diploma, or equivalent) in computer science, physics, engineering, mathematics, or comparable field Profound knowledge in the field of machine learning, in particular deep
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Science, and their responsible applications. Ideal candidates will have: An M.Sc. degree (or equivalent) in Computer Science, Statistics, Mathematics, or related fields. Strong programming skills in Python
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enrolled student in Computer Science, Mathematics or a related field strong programming skills in Python experience in the development, training and fine-tuning of neural networks (e.g. LLM) familiar with
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available in the further tabs (e.g. “Application requirements”). Programme Description The research and training programme focusses on the mathematical and algorithmic foundations of reliable AI along with
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in Computer or Data Science, Mechanical Engineering, Mathematics, Physics, Electrical and Information Engineering, or a related field – very good programming skills in Python – interest in topics
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or Leipzig University. AI is viewed as an interdisciplinary undertaking that requires talents in computer science, engineering, mathematics, social science, law, the natural sciences, and application areas
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bring to the table student in Computer or Data Science, Mathematics, Physics, Electrical and Information Engineering, or a related field strong background in the areas of Machine Learning, Deep Learning