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environment such as Matlab/Simulink. Design and develop an intelligent energy management system for the e-vessel microgrid for coordinated control of multiple energy sources considering cost, carbon, and
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of HLA donor search algorithms and other non-HLA factors, such as ABO, age and CMV status. Analyze donor searches and apply current donor selection algorithms and strategies. Responsible
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that reduce raw data at the sensor level. You will develop AI and machine learning algorithms for anomaly detection, pattern recognition, and efficient data compression. To ensure practical usability
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from multiple systems and sources to answer key operational questions. Deep experience using a variety of data mining/data analysis methods to build and implement dashboards, models and algorithms
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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across disciplines. The candidate will be attached to both FORM (hosted by the Department of Mathematics and Computer Science) and DIAS and should be prepared to engage in multiple and diverse research
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of AI for the integration of multimodal healthcare data specifically incorporating patient preferences. This includes investigating new methods but also designing and benchmarking integration algorithms
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designing and benchmarking integration algorithms. The position is situated in the Intelligent Data Engineering Lab and will be supervised by Dr. Jan-Christoph Kalo and Prof. Paul Groth. What you will do
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. Numerical Analysis and Scientific Computing, developing numerical methods and algorithms that are accurate, efficient, and robust with respect to challenges that manifest in the simulation of complex
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GPR and EMI imaging methods at multiple scales to enhance our understanding of the soil–root system Designing and implementing novel inversion algorithms for GPR and EMI data Identifying links between