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Project advert Modern artificial intelligence (AI) increasingly relies on combining multiple sources of information, such as sound, motion, images, and sensor data, to achieve robust and intelligent
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container platform on Google Cloud. Our technology stack is Python, Django, React, Typescript, GitLab, Terraform, and Google Cloud. We use boilerplate solutions and turnkey templates to scale efficiently
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Engineers, working with a wide range of stakeholders across multiple disciplines and institutions. What you will do You'll be working on a diverse range of AI and HPC platforms and infrastructure projects
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on. Essential Attributes/Qualities Highly proficient in Python Able to produce fast, reliable and repeatable tests that enable CI/CD Hands-on experience in writing, maintaining and executing test automation using
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. • Developing AI/ML-based causal inference models for real-world evidence applications (e.g., target trial emulations- TTE) • Developing data integration methods to combine multiple massive datasets
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for mental health. Excellent programming skills (preferably in R or Python) are essential. The candidate should have experience with at least one of the following: a) working with real-world social media data
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clinical research workflows, with a specialized focus on business intelligence and analytics. The analyst will design and enhance the Clinical Research Data Mart by integrating data from multiple sources
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to continue learning, with the ability to manage multiple projects simultaneously. The position offers opportunities for skill development and to take on leadership roles in individual research projects
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software engineer. You have solid experience in multiple programming languages, in particular Python. Experience working with plugin systems is a big plus. Familiarity with continuous integration pipelines is an
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 10 hours ago
, lagged variables), aligning data with external information such as time-of-day and day-of-week, and organising the data so it can be easily reused for multiple models and experiments. With this prepared