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foundational and applied topics in computer vision and machine learning, with particular strengths in inverse problems, generative models, and geometric deep learning. We work across diverse application areas
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description The postdoctoral project is focused on development and the exploitation of machine learning tools to accelerate the analysis of microtomography data at the MAXIV synchrotron facility. MAXIV
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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collaborate with multiple stakeholders and conduct applied research in silviculture, forest ecology, pathology, policy and planning. We teach bachelor, Masters and PhD level courses addressing all
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included, but for no more than 20% of working hours. The position offers the opportunity to undertake three weeks of training in higher education teaching and learning. The purpose of the position is to
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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letter: - Protein expression in prokaryotes and/or yeasts - Experience with multiple protein purification techniques - Development of recombinant protein production protocols
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mathematics, ecology, history, climatic and medical sciences in collaboration across multiple institutes. An integral part of the project is to develop process-based eco-epidemiological models considering
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, decentralization and mission execution. The RAI team has a strong European participation in multiple R&D&I projects, while RAI was also participating in the DARPA SUB-T challenge with the CoSTAR Team lead by NASA
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characterization methods (potentially including electrochemistry, multiple microscopy modalities, quartz microbalance, impedance spectroscopy) as well as surface modification and characterization and protein