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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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Organoid Engineering for Multi-Organ Interaction Studies (POEM) program ( www.uni-heidelberg.de/en/cctp-poem ) brings together expertise in material science, computer science/machine learning, biophysics
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, conservation genomics, museomics, metagenomics, annotation, machine learning, Instruct users in the usage of hardware and software for molecular biodiversity research, Acquire substantial third-party funding
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terrestrial system models, for example using data analysis methods, such as data assimilation, physical- or process-based machine learning, or deep learning algorithms Analysis of the effects of human
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projects on metabolic diseases * Develop and apply machine learning models for biomarker discovery, patient stratification, and prediction of disease trajectories * Collaborate with clinicians
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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Interaction Causal Models and Inference Time Series Modelling Multimodal Data Integration and Modelling Image Recognition and Computer Vision Computational and Simulation Science Visualisation High-Performance
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for multimodal inferences, combining computer-vision, environmental parameter measures and DNA data. Your role will be central in data acquisition and foremost machine-learning models creation. You will
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looking for student assistants: Leibniz-Project LAB2 (lead by Dr. Levent Neyse) and DFG-Project ‘Mental Models and Discrimination’ (lead by Kai Barron, PhD). Please note: The list of tasks and duties below
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary