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Field
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing
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Project, combined with advanced statistical methods, machine learning techniques and data from wearable sensors. Collaboration with clinicians, data scientists, engineers, researchers and patient
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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
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strong affinity for language data; solid programming skills (e.g., Python) and experience with machine learning or NLP, ideally including transformer-based models and word embeddings; excellent English
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-cell imaging, multi-omics profiling such as transcriptomics, proteomics, and metabolomics, single-cell and spatial analyses, multiplex biomarker quantification, functional studies, machine learning
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, microelectronics, integrated-circuit design, computer engineering or a closely related discipline In addition for Research Associate A PhD in electrical or electronic engineering, microelectronics, integrated
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AI hardware beyond traditional computing architectures. Gain a unique combination of skills in mathematics, machine learning, and photonics. Be part of a multidisciplinary research team spanning
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— for example, a landscape that changes with climate, or a local action that produces effects in another part of the system. This PhD project investigates how people learn, update, and use spatial knowledge when
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8 Jul 2026 Job Information Organisation/Company Luxembourg Institute of Science and Technology Research Field Environmental science Researcher Profile First Stage Researcher (R1) Positions PhD
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feedback modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. Description of the SPARK4B+ project The position is within the EU