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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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expertise in large language models, foundation models, or modern AI systems and they will possess sufficient specialist knowledge of LLMs, foundation models, deep learning, NLP, mechanistic interpretability
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high-dimensional biological data. Preferred Experience developing deep learning models for multimodal biomedical data. Experience with imaging, single-cell, spatial transcriptomics, electronic health
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need to match every topic or method listed below. We value deep expertise in at least one area, intellectual range, scientific ambition, and the ability to collaborate across computer science and the
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply
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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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have knowledge of software development principles, and machine learning, including deep learning experience, as well as a strong background in research processes, including report writing, and producing
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | about 22 hours ago
, the LISA Consortium, and the LISA Distributed Data Processing Centre, where the department leads waveform generation and the global-fit deep analysis. Your tasks Research areas Analytical modeling
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other field or laboratory instrumentation Apply appropriate behavioral, statistical, econometric, causal, spatial, machine learning, deep learning, computer vision, time-series, or mixed-methods