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Field
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education programme Carry out high quality research Develop material integrity predictive framework using different experimental and simulation approaches Participate in project meetings with supervisors and
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Job Code: 30/2026 Job Offer from August 14, 2026 At the Max Planck Institute for Human Development, we study how and why people develop differently over the course of their lives, and how physical
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. These differences introduce distribution shifts that can degrade model performance and reliability over time, particularly in “one-to-many” supervision settings where a single human operator oversees multiple agents
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topology changes and transitions between different operating modes also constitutes a major challenge. Furthermore, this thesis will focus on the frugality of the proposed approaches, seeking to reduce the
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. Demonstrable experience with data processing, signal analysis and programming (e.g. Python/Matlab). Ability to work independently as well as in a multi-disciplinary team. Fluent in English (oral and written
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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
and broaden the scope of geophysical models. We will develop a neural solver that dynamically learns to solve the momentum conservation equations. Unlike traditional supervised learning approaches, our
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deep learning, with a sound understanding of experimental research methodology documented through coursework, projects, or publications. You must have strong programming skills in Python and experience
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/phd ) Documented experience with Python or another relevant programming language, or with computational materials science/chemistry. Good oral and written presentation skills in English. PLEASE NOTE
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. Demonstrated strong proficiency in programming (e.g., Python, PyTorch/JAX, or similar) and computational skills are required for this position. Candidates must have excellent interpersonal and communication
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mechanisms, flexibility markets, and business models for distributed energy resources. Assess the economic viability and market performance of different Local Energy Community configurations. Collaborate in