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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month ago
to fill a PhD position (m/f/d) by 01.01.2027. The Max Planck Institute for Dynamics and Self-Organization at Göttingen, Germany, is an international research institute. It provides an interdisciplinary
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Mathematics » Applied mathematics Physics » Computational physics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 5 Oct 2026 - 23:00 (Europe/Madrid) Country Spain
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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coursework in multivariable calculus, matrix algebra, probability, and statistics (required) Previous experience estimating econometric models (strongly preferred) Interest in applying to a quantitative PhD
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. There is also a need for accounting noise in Deep Learning models and quantifying uncertainty in real-world applications. This PhD studentship (scholarship) leverages large-scale Earth Observation data
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acquire in the course of the PhD skills in live microscopy, experimental neurobiology, genetics and behavioural work. The student will receive mentoring and have the chance to guide the research and
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Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 31 Oct 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded
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that works on the fine modeling of lithium-ion and sodium-ion batteries. The team contributing to HIPOBAT includes the present position, a PhD student, another researcher, a research engineer and an associate
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consumption ecosystems? Job description If this question sparks your interest and you are motivated to pursue a PhD in circular consumption niches and the transition to circular consumption, you might be our
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on clinical complications, and use machine learning to develop and validate predictive models to identify high-risk patients. The research aims to individualise inpatient care, reduce hospital-acquired