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Field
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the areas of stochastic analysis and computational methods towards machine learning with focus on risk-sensitive decision making and control. Techniques may include forward, backward stochastic differential
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combinations of structural and functional properties, using both simulations for machine learning and experimental validation. Fabrication tools and methods are already established in our laboratory. Key
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also be working with machine learning techniques to develop emulators for the theoretical predictions of various observables as function of cosmological parameters. The candidate will develop and use
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(REA) through EU Call HORIZON-WIDERA-2023-TALENTS-01: We are looking for applications for the following role: - Research PhD fellowship on novel microfluidics and machine learning tools for unravelling
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construction, and phylogenetic placement Ability to assess where machine-learning approaches may complement existing bioinformatic and phylogenomic methods, particularly for improving taxonomic resolution Skills
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/drc/ ). About the role The role will contribute to on-going research at the UCL Hawkes Institute to develop advances in computational modelling of neurodegenerative disease, machine learning, and big
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simulations, machine-learned force fields, and artificial intelligence (AI). The successful candidate will lead the development of a computational platform that unifies first-principles methods, classical
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receptor (CAR) therapies Lab website: https://www.pathology.med.umich.edu/chang-kim-lab/ Required Qualifications* Candidates must possess a Ph.D. in life science areas High productivity demonstrated in
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Medical - Clinical Medical - Research Internal Number: A-179907-11 General Description The laboratory of Gislin Dagnelie, PhD, Lions Vision Research and Rehabilitation Center, Johns Hopkins Wilmer Eye
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: Education: Bachelor in Biosciences, or Engineering degree in Computer or Data Sciences. PhD in bioinformatics, data sciences, machine learning or related areas. Experience: previous experience working with