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deep learning. You will support the development of an improved forest RTM that can exploit LiDAR full-waveform data along with hyperspectral signatures. You will plan and carry out field campaigns in
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evaluate innovative methods based on generative models and Vision-Language Models. Design, implement, and validate deep learning approaches for vision applications. Publish research results in leading
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on machine and deep learning methods for analyzing the heterogeneity of microbiota and inferring activities of biological pathways. The Institute provides an international and interdisciplinary research
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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). Deep learning has been used to perform this mapping from UAV (Batista et al., 2025; Chudasama et al., 2024; Lambert et al., 2025) or satellite imagery (Mattéo et al., 2021), at both very high resolution
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discipline Strong experience in integrating several of the following components: Deep learning and LLMs for molecular biology Vision foundation models for pathological image analysis Multi-omics datasets (e.g
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analysing multimodal deep learning models for time-specific cancer risk and time-to-event prediction by integrating imaging with longitudinal Electronic Health Record (EHR) signals. Building scalable
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, and train deep learning models on the resulting data to design new antibiotic compounds that evade both current and likely future resistance mechanisms. Your computational work will directly steer
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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time