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Back to Overview Research Assistant / PhD Student (m/f/d), Machine learning chiral molecules, 75%Full PhD Working LanguageGerman, English LocationKassel Application Deadline20 Feb 2026 Starting
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is
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, and computational models. This PhD position is centered on addressing these challenges through innovative computational methods, combining optical system design, signal processing, machine learning, and
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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a
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Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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-cell transcriptomics, or spatial tissue profiling data, and are keen to develop new methods, for example using machine learning. You have a proven track record of independent research funding and high
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
the department and teach courses if they choose to do so. Education and Experience: Applicants should have (or expect to receive before the start of the position) a PhD in Computer Science or EE/ECE on a topic in
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia