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assays, complemented by mass-spectrometry-driven chemical profiling and machine-learning-supported multivariate analysis. Where relevant, CRISPR-Cas-based genetic perturbations in mammalian cell models
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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for machine learning, e.g. PyTorch or TensorFlow. Strong ability in spoken and written Swedish. Assessment of the applicants will primarily be based on scientific merits and potential as researchers. Special
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, REddyProc) Excellent numeric and analytical problem-solving skills, including time series analysis and statistics (e.g. mixed effects modelling) Capacity to develop computer code and experience with
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factor that strongly modifies turbulence, pressure drop, and heat transfer. Unlike conventional machined roughness, AM roughness is characterized by randomness, porosity, and powder adhesion, producing
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the project’s research area Training in higher education teaching and learning Experience teaching Swedish and European economic history Documented administrative ability Assessment criteria The School
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, tissue sections, RNA/DNA, tabular data) for predictive modelling using software such as Python Documented experience of neural networks, image processing, deep learning algorithms, and data visualization
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higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create the opportunity of further development. The position requires undertaking
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be included, but up to no more than 20% of working hours. The position shall include the opportunity for three weeks of training in higher education teaching and learning. The work duties include
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learning–based protein design, for the successful design of 2D lattices. These methods will then be applied to generate designs targeted for experimental evaluation. Work duties The main duties involved in a