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Field
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skills Evidence of critical thinking, problem solving, and an ability to learn new concepts or methods A willingness to work with quantitative evidence and develop skills in statistical analysis and
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be favorably considered. If you are a non-Swedish speaker, we expect you to try to learn Swedish when you start your position (SLU arranges Swedish courses). Emphasis will be placed on personal
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
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working conditions and attractive benefits. Equality, diversity and equal opportunities are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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or heterogeneous environmental datasets Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience
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and ability to communicate clearly Merits include: Knowledge of LLMs, deep learning, and Python programming Knowledge of power electronics Experience in modelling, simulation, and experimental work In
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candidates to be interested to learn these practical skills. While the project has and initial plan with funding from the Swedish Research Council (see below), the different parts of the project will be
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learn high-end techniques, especially for cryogenic studies, and to build a scientific network in astrobiology and planetary science fields. In addition to the research assignment, participation in
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and eager to acquire the relevant techniques over the course of the PhD. Solid working knowledge of cell wall integrity signalling in plants. Experience in RNA isolation from diverse organisms and