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unsupervised learning, transfer learning, and anomaly detection. Collaborate with process engineers and experimentalists to validate models, demonstrate them at pilot scales, and transfer technology to industry
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well as supervise undergraduate students. The project is embedded in a dynamic and collaborative research environment at KTH, within the Light & Matter Physics division and the Quantum Technologies & Materials group
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colleagues at Lund and international collaborators to establish single-cell or single-nucleus RNA-seq transcriptomic sequencing pipelines. They will use bioinformatic approaches to analyse the resulting data
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research and education in the general area of computer science, including curiosity-driven research and targeted research in collaboration with industrial partners, traditional university education with a
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with access to advanced facilities, platforms, and strong industry collaborations. Full-time (1 FTE) appointment on a fixed term basis for 2 years. Base salary range: Level A, $112,371 p.a. (pro-rata
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development of transferable learned representations from large-scale public omics data and metadata, and the software infrastructure needed to build, maintain, and distribute them at community scale, including
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Foundation Models, to the sPHENIX experiment Perform physics analysis using sPHENIX data Collaborate with physicists and computer scientists on publishing research findings Required Knowledge, Skills, and
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research.We teach. We explore and we discover. We collaborate and lead. We innovate, inspire, and empower. We achieve our potential and create circumstances that help our students and others achieve
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conducting independent research, publishing in international scientific journals, contributing to project development, and collaborating with our public collaboration partners, other researchers, and students
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Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data