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Infrastructure? No Offer Description Area of research: PHD Thesis Job description: Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use
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interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in international conferences to present your own work, and learn about state-of-the-art machine learning and
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, energy systems, or material sciences A Masters degree with a strong academic background in mathematics, computer science, physics, material science, earth science, life science, engineering, or a related
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of results through high-quality publications and open-source software contributions Your Profile: Master’s degree in chemical engineering, biotechnology, computational biophysics, bioinformatics, data science
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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
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, earth sciences, energy systems, or material sciences University degree (M.Sc. or equivalent) in applied mathematics or in computational engineering science, computer science, simulation science with a
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planning and execution architecture for information-driven experiment steering (closed-loop control) Work in an interdisciplinary team of engineers, computer scientists, and life scientists Present your work
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-computing hardware Work in an interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in international conferences to present your own work, and learn about state
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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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, computer science and earth science/engineering, or a related field Proficiency in at least one programming language (Python, Matlab, R, C++, Julia, …) Good analytical skills with a sound understanding of data