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Bayesian optimization and other active learning techniques to guide experimental efforts by identifying optimal chemical compositions and processing conditions of membranes that maximize both selectivity and
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) Experience with probabilistic planning, Bayesian methods, or sensor fusion. Experience in field robotics or marine robotics. Experience with embedded systems or hardware integration. Willingness to obtain a
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. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and compressed sensing
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Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine
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candidate will show in-depth methodological and applied knowledge in the field of machine learning, especially deep learning, experiences in the area of uncertainty quantification, generative and Bayesian