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implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation
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with catalysis/photochemistry Programming skills using Python and MATLAB Analysis of complex scientific data through machine learning What you will do Plan experiments together with your supervisor and
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networks Scientific programming for simulation, data analysis, and reproducible workflows (e.g., Python/Julia/Matlab/C++) Machine-learning–inspired methods for reservoir/neuromorphic computing and
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will use advanced evaluation techniques, data mining, and generative machine learning models to create an active learning cycle to identify materials with adequate properties. Promising materials will be
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Computer Science, primarily within the area of machine learning. This is a temporary position at 50% during six months (the percentage and duration may be adjusted depending on starting date). For information about
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) processing, robotic applications (hardware/software), excellent programming skills (including lower-level languages, such as “C”), and experience in neural networks / machine learning. After the qualification
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Systems, Internet Systems and Computer Networks, Robotics, Machine Learning and AI, Automatic Control, or Mathematical Modelling and Optimization. Documented good profiency in software development. Good
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advanced level (higher education) in the research subject or equivalent competence. Experience with deep learning and machine learning tooling.· In-depth knowledge of LLMs and Transformer architectures
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application! The position Linköping University has a new cybersecurity lab that includes both a computer room for students and a server environment that can be used to simulate different scenarios related
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at Sahlgrenska Academy of relevance include genomics, metagenomics, culturomics, proteomics, transcriptomics, software development, machine learning, and other statistical analyses of large-scale health data