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knowledge of C++, Python, and R. Who we are At the Department of Agroecology, our main goal is to contribute to sustainable solutions to some of the world’s biggest problems within the areas of soil, plants
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Python and experience with deep learning frameworks (e.g., PyTorch) Experience in working with time-series data Good understanding of Internet of Things, wireless communication network and systems Strong
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Python. Be the main author in at least one journal publication in the area of AI4SE, published at a high impact journal. Be the main author in at least two journal publications published at a high impact
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transcriptomics. You possess strong programming skills in Python, R, or another relevant language. Your experience includes working with bioinformatics tools and databases, as well as multi-omics data integration
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-reviewed journals Experience with one or more scripting language (e.g., R, Python, SAS) and/or genetic software (e.g., DMU, ASReml) Can biologically interpret results and relate to breeding decision making
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assessments and modelling of food web dynamics and disease screening. This position provides a unique opportunity to contribute to our understanding of how multiple stressors including climate change
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-cell level, even in low-cell-number CSF samples. Such a method would enable high-resolution immune profiling with potential application to diseases like multiple sclerosis (MS), Parkinson’s disease (PD
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multidisciplinary team environment. Further, we will prefer candidates with some of the following qualifications: Solid background in programming using Python (PyTorch, TensorFlow), R or other languages. Experience
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, electrical engineering, communication engineering, computer science, or a related field. Documented experience with deep learning techniques (e.g., CNNs, Transformers) Strong programming skills in Python and
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energy system. Our work covers coordinated planning and operation across multiple energy systems, renewable energy production, infrastructure, and end-use consumption. The section is in a phase of rapid