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, starting on the February 1, 2026 for the duration of 3 years. Specification: The successful candidate will work on developing physical models of protoplanetary discs, including chemical models to calculate
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ljungdahlii, for which we have already established robust genetic and process engineering platforms. The following publications illustrate the potential and direction of our work: Lauer et al. (2022): Metabolic
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the code “CNTR_25.2” (e.g. “Mustermann,Max_CNTR_25.2”). Please note that there is no application deadline for this application process. Applications will be reviewed in an ongoing manner until
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guidance and support for employees, doctoral candidates and students Your Profile: Master`s degree with subsequent PhD in theoretical physics or a similar field Background and strong interest in developing
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 8 days ago
intriguing for you. Your profile Successful candidates have strong skills in computational molecular (bio)physics, structural biology and scientific computing, as well as a keen interest in
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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological
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array of topics in the focus areas of energy, infrastructure, environment, materials, and chemistry and process engineering. The advertised position is part of the newly founded division 4.2 ‘Material
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of the application and selection process, please refer to the privacy policy on our homepage at https://www.senckenberg.de/en/imprint/ Please visit our website at www.senckenberg.de for further
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85748, Germany [map ] Subject Area: Physics / HEP-Phenomenology (hep-ph) Appl Deadline: 2026/01/31 11:59PM (posted 2024/08/04, listed until 2026/01/31) Position Description: Apply Position Description
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. Please indicate in your application which of the above listed projects is most intriguing for you. Your profile Eligible candidates have strong skills in computational molecular (bio)physics, statistical