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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
using first-principles simulations software (density functional theory and related codes) # Automated Workflows:Utilize automated workflows on high-performance computing systems for efficient data
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false positives and maximizing robustness. Validate the pipeline using diverse and regulatory-relevant samples, supporting future accreditation. You will work closely with a multidiscip... For more
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project “ALPS - AI-based Learning for Physical Simulation”. Expected start date and duration of employment This is a 2–year position from 1 October 2025 or as soon possible. Job description You will be
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Student or Postdoc (f/m/x) in the field of Theory and Methods for Non-equilibrium Theory and Atomistic Simulations of Complex Biomolecules Possible projects are variational free energy methods
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look forward to receiving your application via our online portal: mtj.jobs/3019439 Please submit your application online and including the following files: 1. Motivation letter 2. CV 3. Copy
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education and past and previous employments. A list with your publications. A copy of the PhD certificate and other relevant degree certificates. Names and contact details with e-mail addresses of two
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describing your scientific background and goals for this post-doctoral period (max. one A4 page), a CV including a full publication list, a copy of the PhD certificate (or proof of a scheduled PhD defence
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scientific peer-reviewed publications considered by the candidate as most important for this position. Please note that a copy of each publication must be attached as a pdf file. It is permitted to merge
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: Curriculum Vitae List of publications and manuscripts in preparation (if applicable) Copy of PhD degree certificate, if available Cover letter including: Self-assessment of your academic achievements (e.g. top
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with