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Field
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Apply now The Faculty of Science, Leiden Institute of Advanced Computer Science, is looking for a: PhD Candidate, Efficient LLM Algorithm, Hardware and System Design (1.0 FTE) Project description We
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close-to-field conditions, and (ii) a fully autonomous phenotyping robot, Phenomobile.v2+, equipped with a set of sensors (LiDAR, RGB, IR, and Spectrometer) that enable advanced plant measurements
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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, characterisation and/or calibration of image sensors. Good understanding of, and demonstrable experience in, writing bespoke data acquisition and data analysis code and/or software algorithms with a focus on
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will work in a group with other students and take on specific tasks. The aim is to analyse the robot's capabilities and to implement algorithms that enable the robot to be used sensibly in applications
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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aims at addressing computational challenges associated with data acquisition and information extraction from complex sensors and sensor networks. Crucially, uncertainty management and quantification
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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domains for applications to the mining industry and beyond. We have an opportunity for a passionate and skilled research scientist to design and develop cutting-edge algorithms and fused sensor systems
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algorithms, like multi-camera approaches or sensor fusion. You will implement currently available methods in this area, evaluate their performance and systematically study their dependability. Onsite