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Loïc Roch is a highly experienced software engineer and system architect with a strong background in operating systems, distributed systems, and low-level programming. He has a proven track record of designing and implementing complex software solutions, often contributing to foundational technologies. His expertise spans from kernel development to building robust, scalable systems. Loïc is passionate about tackling challenging technical problems and pushing the boundaries of system performance and reliability. He is also recognized for his contributions to research and his ability to bridge the gap between academic concepts and practical industry applications.
Loïc Roch's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Played a pivotal role in the architecture and development of a novel operating system, focusing on security, performance, and resource management, leading to significant advancements in system efficiency.
Authored and co-authored several influential research papers on topics related to operating systems, memory management, and concurrent programming, presented at top-tier international conferences.
Led the design and implementation of critical infrastructure components for a high-performance computing environment, enabling complex simulations and data analysis for scientific research.
Harvard University - Year 2017
University of Zurich - Year 2012
Ecole polytechnique fédérale de Lausanne - Year 2010
Ecole polytechnique fédérale de Lausanne - Year 2007
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Atinary Technologies is a Swiss-American deep-tech company developing AI-powered solutions and cloud-based platforms to accelerate scientific discovery and innovation in R&D labs. Their flagship product, the Self-Driving Lab™ platform, integrates machine learning algorithms with laboratory automation systems to optimize experiments and discover new materials, chemicals, and formulations faster and more efficiently. They aim to empower scientists and researchers in industries like pharma, biotech, chemicals, and materials science by automating complex R&D workflows and enabling data-driven decision-making.
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