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communication skills in English are mandatory Commitment, teamwork and a critical mind The ideal candidate should have experience in some of the following topics: Experience with control methods: impedance
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budget and time constraints, facilitating flexible adoption of the methods. The project is carried out in close collaboration with Helical-AI, an industrial partner specialized in large-scale genomic
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include Luxembourg and its neighbour regions. Using an innovative multi-method design, the project integrates: Daily diary and ecological momentary assessment (EMA) approaches In-depth qualitative and
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of qualitative and/or quantitative research methods Experience in applied social science statistics is an asset Capacity to work to with interdisciplinary collaborative teams Capacity to work with public
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Methods for Enhanced Market Intelligence, Automated Market Research Summaries via RAG-enabled AI. The successful candidate will extensively explore and analyse the suitability and potential benefits of new
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in the context of the microbiome. Here, using a triptych approach leveraging in silico, in vitro and in vivo methods, we aim to specifically and systematically characterize and validate microbiome
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evaluating adaptive Medium Access Control (MAC) and network‑layer protocols to enhance performance in shared spectrum environments. Developing AI‑driven methods for real‑time interference prediction and
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of de-anonymization, including researching current de-anonymization strategies Investigate synthetic data generation methods and their utility Deploying and benchmarking the above-mentioned methods