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and simulation Prior experience with particle accelerators and/or FELs is highly desirable Familiarity with machine learning techniques is a plus but not necessary Excellent command of English is
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Research FieldEngineering » Materials engineeringEducation LevelPhD or equivalent Skills/Qualifications To acquire further scientific qualification (habilitation), the job holder should be able to cover a
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development opportunities (e.g., heiTRACKS) Competitive salary and extensive benefits Possibility to teach in the area of your expertise (English or German) The remuneration is based on pay grade TV-L 13. We
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environment that provides equal opportunities. We are convinced that diverse teams and a variety of perspectives enrich our work and our daily collaboration. In a continuous process of learning and reflection
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opportunities through courses, e-learning programs, and coaching sessions Structured onboarding: A systematic introduction to your new role and team Healthy at work: A wide range of health and wellness programs
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infrastructure and outstanding career development opportunities (e.g., heiTRACKS) Competitive salary and extensive benefits Possibility to teach in the area of your expertise (English or German) The remuneration
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general approaches from cognitive science (resource-rational analysis) to investigate in how far LLMs may acquire “causal world models”. The focus of the project is on the latent representations in LLMs
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in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python and/or C/C
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 3 months ago
Studies (CAS ) at the MPE invites applications for an Astrochemistry Postdoctoral on computational chemistry. A particular focus of the project is on utilizing machine learning techniques for molecular
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at international conferences Ability to work in a team Goal-oriented and structured approach to work Independent working style and commitment Willingness to learn and interest in interdisciplinary applied research