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. or equivalent) in applied mathematics or in computational engineering science, computer science, simulation science with a strong background in applied mathematics Excellent programming skills (Python, C/C
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-of-the-art machine learning and computer vision methods and their applications Your Profile: Excellent Master’s degree in engineering, computer science or mathematics (or a related field), with a focus
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, computer science, or mathematics (or a related field), with a focus on robot vision and control, image processing, or machine learning Solid mathematical and physics background, distinct analytical skills
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) in applied mathematics or in computational engineering science, computer science, simulation science with a strong background in applied mathematics Excellent programming skills (Python, C/C++) Good
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domains: life and medical sciences, earth sciences, energy systems, or material sciences University degree (M.Sc. or equivalent) in applied mathematics or in computational engineering science
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international conferences Supervise student theses Your Profile: Excellent Master`s degree with a strong academic background in computational engineering, mathematics, computer science, physics, engineering or a
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engineering, biotechnology, computational biophysics, bioinformatics, data science, or a closely related discipline with a strong academic record Genuine interest in data-driven and physics-based modeling
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, computer science, physics, material science, earth science, life science, engineering, or a related field Proficiency in at least one programming language (Python, R, C++, Julia, …) Good analytical skills with a
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benchmarking datasets will be released through an open-source library. Your Profile: A Masters degreee with a strong academic background in physics, mathematics, computer science, or a related field Proficiency
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, computer science, or a related field Proficiency in at least one programming language (Python, C++, …) Experience in neuroscience is an advantage Good analytical skills with a sound understanding of data evaluation