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Field
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-time sensing, multi-sensor fusion, and intelligent algorithms can jointly enable safer, greener, and smarter rail operations. Key research topics include eco-driving, environment cooperative perception
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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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’ algorithms, however these may not provide physically interpretable results or quantifiable uncertainty. We propose developing data pipelines combining advanced preprocessing techniques, statistical tools, and
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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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of peptide design and chemistry, computational methods (machine learning, deep learning, genetic algorithms), microbiology, synthetic biology, and related areas essential to developing novel
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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete
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algorithms, have excelled in tasks like computer vision, image recognition and large language models (LLM). However, their reliance on extensive computational resources results in excessively high energy
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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
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design
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an optimal molecular representation (including data procurement) and integrating generative model and binding oracles. Propose an algorithm to bias the generative models towards desirable properties, such as