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Field
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Background in probabilistic methods Experience with the application of AI algorithms and probabilistic methods Good programming skills Personal characteristics To complete a doctoral degree (PhD), it is
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advanced statistical machine learning, reinforcement learning, and gen-AI-driven decision models for supply chain and operations optimization. • Design scalable algorithms for demand forecasting, risk
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of ne computational chemistry algorithms in efficient computer codes • Demonstrated ability to approach complex problems creatively • Experience and/or interest in mentoring undergraduate
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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and empirically oriented, focusing on how political ideas, actors, and conflicts are shaped and mediated through digital platforms. Central themes may include, for example, algorithmic influence
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should operate in an AI context. The AI revolution has sparked ongoing debates that highlight the multifaceted role of AI and algorithms in shaping our world—in ways that engage deeply with law. Fully
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of solvers for stochastic optimization problems, and test the methods on real-life data. As part of the PhD you will be following advanced courses to extend your skills, implement and test algorithms, and
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interface, and all the way to quantum algorithms and applications. The long-term mission of the programme is to develop fault-tolerant quantum computing hardware and quantum algorithms that solve life
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set of alternative ways of evaluating a particular expression with unspecified matrix sizes. When a concrete expression is evaluated at run-time, thus revealing the matrix sizes, an extraction algorithm