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will be developed. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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intelligence, computer algebra, computational logic and automatic reasoning Experience with SAT, SMT, QBF, MaxSAT Experience with algorithmic methods for commutative and noncommutative polynomials Experience in
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No: 101227148) and coordinated by the University of Bergen, Norway. Supervisors: Prof. Martin Reincke , Prof. Nicole Reisch Location: Ludwig Maximilians University Hospital Munich, Germany Duration: 3 years
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. Supervisors: Prof. Nicole Reisch , Prof. Martin Reincke Location: Ludwig Maximilians University Hospital Munich, Germany Duration: 3 years (with possibility of extension) Start date: August 2026 at latest
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Algebra, Programming, Data Structures and Algorithms) and AI-related courses (Deep Learning, Feature Engineering). Candidates with experience in university teaching or R&D in related industries is an
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will be based in Odense, under the primary supervision of Prof. Ricardo J. G. B. Campello , but they will be expected to also work closely with other PhD students, postdocs, and collaborators both from
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highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times
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practice while supporting complex tasks such as image and video enhancement, layered editing, and object-level content manipulation. The candidate will contribute to 1) algorithmic development, including
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high dynamic range (HDR) imaging is redefining the way smartphone cameras and displays capture the world. Despite HDR becoming the new standard, many classic image-processing algorithms and generative