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Field
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workspaces under positional restrictions. Develop smart control algorithms that will allow the robotics end-effectors to communicate with the central control system and coordinate tasks with other end
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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software frameworks, algorithms, robust testing and validation methods, and/or empirically validated solutions that contribute directly to social good, promoting trust, fairness, transparency, and
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control engineering, optimization algorithms Control of drones and flight experiments as well as knowledge in AI / Machine Learning would be an asset Outstanding academic records Teamworking experience, e.g
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. -Machine learning code generation for autonomous translation of payload data semantics. -Dictionary learning and algorithms for translation between major data modeling languages. -Model-based System
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You will explore the nature of metal corrosion inhibitor interactions through advanced molecular modelling and integrate this understanding into the formulation of evolutionary algorithms
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect