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like deal.II and Trilinos). Proven experience in code development is crucial; note that coding skills will be checked during the job interview. Very good knowledge of the theoretical foundations
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for the long-term health of our code repositories and the versioning of models and data (Git, DVC, MLflow). Requirements: Education: Ph.D Degree in Computer Science, Engineering, Mathematics, Physics
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AI-modeling and coding. The successful candidate will work alongside a multidisciplinary team, leveraging artificial intelligence and computational methodologies to optimize treatment plans, enhance
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codes such as ImpactX, GPT, or similar. ● Experience with common PIC codes used in LWFA research such as OSIRIS, FBPIC, or similar. Brief Description: The primary purpose of this position is to
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learning in chemistry would be advantageous, as would familiarity with ML approaches for atomistic modelling (e.g., MACE, ACE, NequIP, PhysNet, reactive MD). Prior contributions to scientific code
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of code to conduct complex comparative genomics, implement HMM searching strategies and conduct phylogenetic analysis on a grand scale while making use of sophisticated phylogenetic methods (for example
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of attoseconds to femtoseconds. The project will involve working with first principles time dependent density function theory (TD-DFT, as implemented in the Elk code) [1-3]. A background including experience in
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of Sciences, 19B Yuquan Road, Shijingshan District, Beijing, China. Postal code: 100049
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and at national/international conferences. Collaborate with an interdisciplinary team of biostatisticians, computer scientists, and climate scientists. Contribute to open-source code, reproducible
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: Writing and testing of code. Planning of research & discussing research with collaborators. Writing papers, and giving presentations. Qualifications: PhD in Chemistry Before applying, please note that to