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aims: Develop end-to-end protocols for screening selected foods and nutraceuticals. Create advanced strategies for data integration using tailored algorithms and machine learning approaches. Demonstrate
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of methodologies, from in-depth behavioral assessments to computer vision, machine learning and neuroimaging techniques, we aim to uncover the complexites of neurodevelopmental disorders. Our
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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01.12.2025. About us The Chair of Agrimechatronics (Prof. Oksanen) studies especially Intelligent Machines for Agriculture. The research themes include topics like tractors and other agricultural vehicles and
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., health and climate/environmental data) and could include a range of data science methods, such as utilising geographical information systems (GIS), statistical analysis, machine learning, deep learning
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ID: PMP_TRR408_C5 Investigators: Prof. Dr. Allister Loder, TUM Professorship of Mobility Policy, and co-supervised by at least one leading international researcher Requirements
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well as new projects. The position may additionally involve implementing data analytics, including machine learning. The successful candidate will have a Ph.D. or equivalent degree in Bioengineering
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with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
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., machine learning, stochastic dynamic programming, simulation). Affinity with (food) supply chain management is preferred. To collaborate with and to co-supervise MSc thesis students and internship students