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Field
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The rapid growth of deep learning has come at an extraordinary environmental and computational cost, yet the standard training paradigm remains remarkably unchanged. Every sample is passed through
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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to plant biosciences where the impact could be huge and as a result exciting opportunities get missed. When we use light to image deep into complex samples there is a common problem that occurs – the light
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informatics, or a related field - Strong programming skills in Python and experience with deep learning frameworks (PyTorch preferred) - Experience or strong interest in large language models, multimodal
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-acoustic waves are weak, noisy and broadband. To address these challenges, this project will employ deep-learning techniques for signal denoising and 3D dose reconstruction. The project is in close
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optimization techniques. You have experience with modern Deep Learning Frameworks (PyTorch, Tensorflow, Jax) and proven ability of CUDA and Python programming. Knowledge of, or prior experience with, optimizing
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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Functions Developing and implementing machine learning and deep learning models to analyze forestry, physiological, and ecological datasets Modeling plant growth, carbon allocation, stress response (e.g