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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 2 months ago
Job Code: MPIDS-W091 Job Offer from July 28, 2026 In the Department of Living Matter Physics (LMP) we seek to fill several PhD positions on Statistical Physics and Active Matter. The Max Planck
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statistics, engineering physics, physics, machine learning, or in a similar subject, or have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including
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of many-body physics, realizing unprecedented phases of matter characterized by their quantum-information content. This doctoral project addresses a central question for quantum technologies: how can the
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quantification, and interpretation. Account for structures arising from spatial relationships, physical constraints, high-dimensional imaging, and clinical covariates. Apply the resulting methods to spatial
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, identifiability, uncertainty quantification, and interpretation. Account for structures arising from spatial relationships, physical constraints, high-dimensional imaging, and clinical covariates. Apply
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safe and respectful working and study climate, and an inspiring environment for education and research. Learn more about our codes of conduct We are located on one physical campus, in the heart
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in practice when the dimension of the data-generating process exceeds the sample size, this project will contribute by developing the distributional properties of generalized inverses of the high
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welcome to contact the person listed at the bottom of the job posting. Further information Read more about our recruitment process here. The assessment of candidates for the position will be carried out by
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find a suitable candidate, we will arrange an interview. We therefore recommend applying at your earliest convenience. Screening is part of the selection process. Website for additional job details https
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temporal variation and cannot be adequately described by models that assume a uniform structure. The project aims to develop flexible models that allow key characteristics of the process, such as event