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and illegal logging detection. Working at the intersection of computer vision, deep learning, geospatial analysis and remote sensing, the postholder will collaborate closely with academic and
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image analysis, data processing and machine-learning-based modelling. More about the position The main purpose of the fellowship is research training leading to the successful completion of a PhD degree
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, and mathematical data science, with an active programme at the interface of dynamical systems, machine learning, and quantum information. The position sits in the GOAL group (GeOmetry, dynAmics, and
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at developing methodological contributions at the intersection of computer vision, multimodal learning, predictive world models, embodied AI, and human-robot interaction. The candidate will work towards models
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lab experiments Publish results in top-tier journals in related fields Assist in mentoring PhD students Job Requirements: PhD degree in mathematics, systems engineering, or machine learning Expertise in
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network (MINDnet)”. The PhD project “Efficient learning algorithms for closed-loop optogenetic control of neural circuits” aims to develop an optoelectronic interface with neural circuits, that optically
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milestones and project objectives. Job Requirements PhD in Quantum Information, Theoretical Physics, Computer Science, Mathematics, Machine Learning, or a closely related discipline. Candidates nearing
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PhD candidate will have: A strong background in computer science, artificial intelligence, machine learning, or a closely related field. A solid understanding of machine learning concepts, particularly
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1. A PhD degree in a relevant area, e.g., Chemical Engineering, Control Engineering, Computer Science, Systems Engineering, etc. 2. The candidates should have strong math, modeling, programming, and
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machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to [email protected] For more information about working at