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deep experience with PyTorch, JAX, or TensorFlow. Broad knowledge of modern ML and optimization (gradient‑based, evolutionary, Bayesian, reinforcement learning). Hands‑on experience with generative
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of the following areas: Wireless and satellite communications AI/ML for dynamic networks including Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models
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architectures for (bio)medical research and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based
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‑on experience with common machine learning / deep learning frameworks (eg. PyTorch or JAX) applied to biological or structural data. Solid Python programming skills, with experience building maintainable and
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
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terrestrial networks, non-terrestrial network entanglement distribution. Your profile PhD degree in wireless communications, signal processing, machine/deep learning or a closely related field in Electrical and
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of novel mechanistic insights is gained through the application of novel probabilistic deep-learning models that automatically extract biological and statistical knowledge from your in vivo perturbational