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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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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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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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meaningful feedback to patients, clinicians and policymakers The PhD will work at the interface of machine learning, deep learning, geospatial AI, causal modelling, and digital health systems. Your Role You
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/ knowledge in the following areas: Solid background in motion planning and control of mobile robots Background in SLAM and SA models Background in Reinforcement and Deep Learning in robotics with a focus on
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About us VIB.AI, the VIB Center for AI & Computational Biology, is a research center dedicated to integrating machine learning with deep biological insight to understand complex biological systems
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processing Graph signal processing Machine learning - supervised, unsupervised and reinforcement and tools such as TensorFlow, PyTorch, Keras and GreyCat Neuromorphic computing, spiking neural networks Deep
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both independently and as part of an international, interdisciplinary team Assets•Experience with computer vision or deep learning (e.g. PyTorch, TensorFlow)•Familiarity with street view imagery or other
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, or computational imaging; You have strong skills in digital signal processing (DSP), including familiarity with machine learning or deep learning; You have proficiency in scientific programming (e.g., Python, MATLAB
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at the interface of machine learning, deep learning, geospatial AI, causal modelling, and digital health systems. Your Role You will develop the core AI and data-driven models that transform large-scale