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biological markers and state-of-the-art deep learning, the research will uncover conserved cellular state transitions and perturbation response programs across biological systems. The successful candidate will
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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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electrocatalytic processes. This postdoctoral position will investigate pulse-mediated electrodeposition of metal particles using deep eutectic and organic solvents, ultimately aiming to induce kinked high-index
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documented expertise in: Modeling and simulation of physical systems, Deep learning with applications in robotics, in particular field robotics, and Control and motion planning of mechatronic systems
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skills. Particularly meritorious is documented expertise in: Modeling and simulation of physical systems, Deep learning with applications in robotics, in particular field robotics, and Control and motion
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Join us at the forefront of life science AI. We are looking for a postdoctoral researcher to develop cutting‑edge, multimodal transformer‑based deep learning methods to extract insight from genomic
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in radiotherapy with the goal of enabling fully adaptive radiotherapy. The work is based on deep learning, where models are trained on generated or clinical data. The project is carried out in
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, tissue sections, RNA/DNA, tabular data) for predictive modelling using software such as Python Documented experience of neural networks, image processing, deep learning algorithms, and data visualization
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the following areas: deep learning, reinforcement learning, imitation learning, robot perception, navigation, and manipulation. Experience with whole-body control, humanoid or multi-DOF platforms, and
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on animal behaviour and welfare in pigs, where behavioural science is integrated with artificial intelligence and deep learning for the assessment of animal welfare. You will have a central scientific role in