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We offer you The project brings together a consortium of leading European institutions, including the University of Neuchâtel, University of Rennes 1, CNRS, BRGM, ENS Paris, Eawag and the University of Basel. The successful candidate will be part of a cohort of 4 PhD students, 2 research...
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characterizing individual nanoclusters • Engineering and purify protein nanopores with tailored sensitivity to size, charge, and etc. • Developing data analysis pipelines and machine learning approaches for signal
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schools, where children and adolescent research participants will complete computer-based learning tasks designed to capture metacognitive monitoring and regulation during task performance. The successful
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theory, physics-based simulation, and machine learning. Job description The PhD project will develop machine-learning methods for atomistic materials modeling Possible research directions include machine
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childhood through adolescence. Data collection will primarily take place in schools, where children and adolescent research participants will complete computer-based learning tasks designed to capture
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the first, the student will develop expertise on the lab's existing machine perfusion platforms for human and rat liver This will include learning the engineering design, assembly, and operation of
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, engineering, and the broader questions of how AI systems affect the people and institutions that interact with them. You should bring: A master’s degree in computer science, machine learning, HCI, or a related
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applicability. By bridging machine learning, behavioural science, and clinical research, the project seeks to establish foundational methods for trustworthy agentic AI systems that can be deployed across diverse
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of the PhD position sits at the crossroads of quantum computing, machine learning, and combinatorial optimization — an area where some of the most exciting and open questions in the field live. Research
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cohort of doctoral researchers and benefit from ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation