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, conference presentations, and engagements with stakeholders. Additionally, you will actively contribute to the institute's academic environment by participating in seminars, workshops, and collaborative
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with experimental and clinical collaborators. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease- and treatment
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Review Date: (To ensure consideration, please submit all application materials before review date) 03/13/2026 Posting Close Date Open Until Filled Yes Description of Work Recognizing that collaboration and
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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning
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Applications are open The PhD project is part of a collaborative project: ‘A “virtual market” for analysing the uptake of energy efficiency measures in residential and commercial sectors’, funded by
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of Anne-Marie Fors Connolly at the Department of Clinical Microbiology, Umeå University, in close collaboration with Martin Rosvall at IceLab and Tommy Löfstedt at the Department of Computing Science. You
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pride in being an inclusive community of outstanding learners, investigators, clinicians, and staff where interdisciplinary collaboration is embraced and great ideas accelerate translation of fundamental
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learning). The project will be carried out in a collaboration between Linköping University (main supervisor: Anders Eklund) and Lund University (co-supervisor: Mikael Nilsson) through an ELLIIT collaborative
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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condensed matter physics • Ability to learn and develop skills in analytical computation, theoretical modelling and numerical simulations, in particular the numerical solution of partial differential