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. This unique position combines advanced finite element modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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patterns of genomic sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models
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combination with machine learning and/or data mining techniques • Explainable AI/ML using visualization • AI/ML-empowered visual analytics of multivariate networks (network embeddings, …) • Large Language Model
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sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models that unlock
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. Eran Elhaik to design machine-learning models that unlock the potential of genomics for forensic investigations and historical reconstructions. Work duties We aim to develop machine learning methods
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required. Proficiency in statistics and programming are highly meriting, especially in gene regulatory networks, machine learning, and bioinformatics tools. Expertise in CRISPR-based assays, especially
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high