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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
but are not limited to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and
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mathematical models to address fundamental questions in biology. Examples of research topics include but are not limited to: development of new AI architectures for biology and hybrid models that combine deep
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in machine learning, computer science, computational biology, statistics, or a related field.Knowledge of modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).Experience developing AI
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an advantage: Deep learning or machine learning Structural bioinformatics Protein structure prediction and modelling Molecular simulations Membrane proteins or membrane biophysics Scientific software development
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an advantage: Deep learning or machine learning Structural bioinformatics Protein structure prediction and modelling Molecular simulations Membrane proteins or membrane biophysics Scientific software development
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in machine learning, computer science, computational biology, statistics, or a related field. Knowledge of modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow). Experience developing AI
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profile You have: a Master degree in engineering, computer science, mathematics or a related field a strong background in machine learning and AI (deep learning, LLMs, computer vision) strong proficiency in
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, statistics, or a related field. Strong experience with large language models, including pretraining, fine-tuning, prompt engineering, and evaluation. Knowledge of modern deep learning frameworks (e.g., PyTorch
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and deep learning techniques. About the role Your role will include: analyzing image-based plant phenotyping datasets (RGB, hyperspectral) designing and optimizing data analysis workflows for automated
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, statistics, or a related field. Strong experience with large language models, including pretraining, fine-tuning, prompt engineering, and evaluation. Knowledge of modern deep learning frameworks (e.g., PyTorch