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Pharmaceutical Roundtable. In this project we will employ deep learning-based protein sequence design tools to deliver biocatalysts for peptide synthesis. These designed enzymes will be further optimized using
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Neutral Infrastructure (dfCO2), this role contributes to Program 4: Machine Learning for Carbon Performance (https://dfco2.org.au/program_4 ) that aims to advance the next‑generation AI methods to model
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specifically detailing your interest in glioblastoma and deep learning Two reference letters For more information:stein.aerts@kuleuven.be Where to apply Website https://jobs.vib.be/j/130090/postdoc-position-deep
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and supervise PhD students. The department has developed several courses within data science, e.g., Bayesian methods, Advanced Machine Learning, Deep Learning and AI methods. You are expected to teach
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PhD project, the successful candidate will develop an open-source workflow using deep learning and hierarchical statistical models to streamline the data flow from acoustic recorders to ecological
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methods (e.g., deep learning, generative models, representation learning) ● Experience working with large public biological datasets/repositories (e.g., GEO, SRA, UK Biobank, GTEx, etc
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scientific curiosity. You thrive at the boundary of robot learning, computer vision, deep learning, and simulation, and you are excited to see your research running on real robots. You communicate clearly
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date specified in AP Recruit to learn whether the department is currently reviewing applications for a specific position. If there is no future review date specified, your application may not be
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supervise PhD students. The rest of your time (40%) is devoted to teaching. The department has developed several courses within data science, e.g., Bayesian methods, Advanced Machine Learning, Deep Learning
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at the intersection of mathematics and AI safety, with a focus on developing rigorous mathematical foundations for AI interpretability. Research directions include mean field theories of deep learning, data attribution