38 programming-"St"-"FEMTO-ST"-"St" PhD scholarships at Technical University of Munich
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skills in programming and strong experience in computational geometric modelling. Expertise in the field of Additive Manufacturing, Construction Robotics, and/or Artificial Intelligence is very beneficial
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academic performance. Experience with stochastic methods, risk and reliability analysis, and data analysis. Programming experience in Python, MATLAB, C/C++, or a similar language. Strong analytical
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team. You will be enrolled in the TUM Graduate School with its structured doctoral program and professional-skills courses. You will contribute to an French-German collaboration on next-generation
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expertise in developing and training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills and
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to design and evaluate programs that enhance community wellbeing. The project is a collaboration between the University of Global Health Equity (Rwanda), TUM, NYU Abu Dhabi, and the Government
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an interdisciplinary career skills programme across Europe. The Technical University of Munich (TUM) is one of the best universities in Europe. It is characterised by excellence in research and teaching
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Profile The ideal applicant has a strong background in bioinformatics or computational chemistry, as well as data analysis and solid English-language skills. Experience with programming is highly
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and networking opportunities within the Munich AI ecosystem and within structured graduate programs: the Munich Centre for Machine Learning (MCML), the Munich Data Science Institute (MDSI), the local
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autonomous systems Fluent English (spoken and written); German proficiency is a strong plus Ability to work independently, willingness to learn and acquire new competence Strong programming skills (Python, C
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: Completed master studies in the field of environmental sciences, forestry, landscape ecology, remote sensing or related fields Interested in remote sensing, quantitative methods and programming Prior