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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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records commensurate with grade Desirable criteria Ability to carry out statistical analysis of genetic data Up to date knowledge of machine learning methods applied to clinical and omics data Experience in
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Design invites applications for a PhD stipend in the field of secure machine learning within the general study programme Electronic and Electrical Engineering; as per November 1, 2026, or as soon as
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Do you enjoy finding solutions to integrate and analyse large data sets of biodiversity dynamics and their drivers? Are you creative and able to couple various data flows and integrated modelling
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in AI, bioinformatics, statistics, computer science, data science, machine learning, epidemiology, or related fields with a relevant PhD degree (required). Experience with analyzing large genetic data
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-preserving machine learning, distributed training, large language models, agentic AI, or cryptographic protocols is advantageous but not a requirement Qualification requirements PhD stipends are allocated
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science, medicine, or a related field. Excellent programming skills in Python and/or R. Experience with data curation, large-scale datasets, and genetic and machine learning methods. Interpersonal skills and
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for translatome analysis Expertise in integrating large-scale multiomic datasets, including machine learning based approaches Excellent written communication skills demonstrated by an outstanding publication track
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students to analysing multimodal learning data (e.g., individual as well as collaborative verbal interactions, student gestures, task and activity sequences) and evaluating long-term learning outcomes. As
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing