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: Masters degree in bioengineering, environmental sciences or applied mathematics Knowledge of plant and soil sciences Mathematical modelling skills Ability to work independently as well as collaboratively in
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, physics, computer science, mathematics, electrical/electronic engineering or a related subject Strong programming skills (Python) Familiarity with machine learning and deep learning frameworks (e.g
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the other language Interest in abstract scientific applications Experience with some of the following: Qt, CMake, git; cross-platform development; numerical mathematics, condensed-matter physics, scattering
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the modelling pipeline Your Profile: A university degree (Master) with subsequent PhD in Applied Mathematics, Mathematical Biology, Computational Physics/Engineering or related fields with strong relation
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masters degree and subsequent Ph.D. degree in Computer Science, Mathematics, Physics Engineering or in a similar field. Alternatively an excellent masters degree with professional experience Very good
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university degree (Master) with subsequent PhD in Applied Mathematics, Mathematical Biology, Computational Physics/Engineering or related fields with strong relation to scientific computing Recent experience
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, or mixed-signal circuits, including SPICE and related tools is strongly desired. For algorithms projects, a strong background in coding and advanced mathematics is strongly desired. Coursework in theoretical
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to educational events, such as university lectures, JSC courses and hackathons. Your Profile: Excellent Master in Computer Science, Mathematics, or Physical Science, Very good knowledge and proven skills with
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have: A completed university degree (Master or equivalent) in data science, computer science, mathematics, materials science, physics or a related subject (or comparable proven experiences) Practical
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Computer Science, Mathematics, Physics Engineering or in a similar field. Alternatively an excellent masters degree with professional experience Very good knowledge and proven skills with larger deep learning