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for analysis of the data you create. Your findings will be a vital part of understanding the evolution of cancer drug resistance in cell models and in patients. About you: You’re excited about our research
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are seeking a highly motivated PhD candidate to develop efficient on-device generative AI systems based on large language models (LLMs). The project focuses on creating compact, low-latency, and energy
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modelling and density functional theory calculations will be used to further increase our understanding of the photo-reduction mechanism. Correlating these theoretical insights with the structure and activity
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attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Gallium Nitride: covering the entire chain from device modelling and
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and national basis offers a perfect start for a scientific or industrial career! The activity includes the conception and development of sustainable ecosystems, in particular for the realization of AI
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on projects that model the work of our full-time employees. You will also go through a comprehensive education program and interact with mentors who are at the top of their field, allowing you to build
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on projects that model the work of our full-time employees. You will also go through a comprehensive education program and interact with mentors who are at the top of their field, allowing you to build
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background in physics, biophysics, biological physics, or bioengineering. This PhD project will primarily focus on experimental research, which will include data analysis and there is scope for modelling
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 2 days ago
have hands-on experience in crop genomics and managing genotyping data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using