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are seeking a highly motivated PhD student to join a collaborative project focused on developing genetic risk scores as biomarkers for neurological disorders. The Rademakers lab leads cutting-edge research
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the digitisation process. You will work closely with the project’s Research Software Engineer and provide material for the digital edition of Woolf’s library according to the project’s schedule. You are prepared
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annotation of links between his reading and his writing. You will report to the project’s PI on a weekly basis. You will work closely with the project’s Research Software Engineer and provide material
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the digitisation process. You will work closely with the project’s Research Software Engineer and provide material for the digital edition of Woolf’s library according to the project’s schedule. You are prepared
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annotation of links between his reading and his writing. You will report to the project’s PI on a weekly basis. You will work closely with the project’s Research Software Engineer and provide material
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(e.g., using Matlab) and in real-life using Software Defined Radios (SDRs). You will write detailed technical evaluation reports on the developed solutions, implementation, and experiments, leading
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industrial Ph.D. position focused on developing scalable, Machine Learning (ML) pipelines for genomic and epigenomic biomarker discovery from Oxford Nanopore Technologies (ONT) long-read sequencing data
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quenching and wavelength-specific energy transfer), this project seeks to develop novel insights into molecular quenching dynamics. This fundamental research project will advance the understanding
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is to develop RL controllers that can learn from data while guaranteeing stable and safe operation in complex, nonlinear systems such as chemical plants and energy processes. By validating the approach
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the genetic etiology and developing diagnostics and treatment methods for these diseases. Approaches range from human genetics, genomics, protein biochemistry and neuronal and glial cell biology to integrative