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and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in scientific journals
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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nanocrystals, hybrid perovskites and 2D materials. Development of new data-driven approaches for studies of optoelectronic properties using EM, including machine learning / machine vision algorithms. The balance
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of computer literacy and knowledge of data management programs. Excellent organization skills, ability to prioritize a variety of tasks, and careful attention to detail. Ability to demonstrate professionalism
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programming languages (Python or C++), machine learning/deep learning, computational chemistry and polymer chemistry is an advantage. You have a good knowledge of English, both oral and written. Your research
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chemistry and study the physicochemical properties of peptides loaded into the materials. Build surrogate models and apply machine learning techniques to extract design rules and rapidly screen thousands
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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning
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Science, Cybersecurity, Artificial Intelligence, Computational Cognitive Science, Data Science, or a closely related field Solid background in machine learning and cybersecurity Interest or prior experience in phishing
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
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the project. Please don’t let that discourage you. Very few PhD candidates start with all the knowledge they will eventually need. Curiosity, motivation, and a strong technical foundation are often much