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Context and Motivation Bilevel optimization problems, in which one optimization problem is nested within another, arise in a wide range of machine learning settings. Typical examples include
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dedicated to discovering and refining the core mechanisms that will enable machines to learn continuously, make robust decisions in complex environments, and evolve autonomously. Key research directions
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machine learning models for the diagnosis of temporomandibular disorders (TMD) based on jaw motion time series data. Moreover, the successful candidate will be affiliated with the Comprehensive Center AI in
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is connected to the vibrant local ecosystem for data science, machine learning and computational biology in Heidelberg (including ELLIS Life Heidelberg and the AI Health Innovation Cluster ). Your
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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl
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, particularly in C++ and Python Good communication skills in spoken and written EnglishInterest or prior experience in machine learning techniques is considered an asset. You may expect a multifaceted and
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simulation techniques will be used to design proteins; a particular focus is binding flexible regions and antibody design, which are challenging for current machine learning approaches. You will become
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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of computational tools including Artificial Intelligence (AI) and Machine Learning (ML) in virtual screening, small molecule design and optimization is preferred. Set up the chemistry strategy for projects and
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Associate Scientist or Lead Researcher - (Protein Engineering and Design, Genome Editing, Biotechnol
protein candidates by structure-guided design and high-throughput screening. Apply new artificial intelligence and machine learning techniques to guide protein engineering design. Analyze experimental