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Field
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skills : We expect a candidate with a strong background in machine learning or statistics. The candidate must also be proficient in high-level languages like Python. Familiarity with single-cell date and
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. This project aims to establish provable guarantees for Human-GenAI-Alignment by integrating statistical methods with adversarial methods. For example, by leveraging PAC methods and conformal prediction, we can
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analysis, with possible specialisations in genomic and molecular biology techniques as well as in algorithms, statistics and artificial intelligence for molecular genetics. This is based on perspective and
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Science, Statistics, Applied Mathematics, or a related field. • Strong background in convex analysis, statistical machine learning (reinforcement learning, LLM and generative modeling), stochastic modeling and
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/2025.03.02.641062 Your Profile The successful applicant should hold a master's degree or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological
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are looking for highly motivated candidates with: A strong academic background in computer science, AI/ML, bioinformatics, or related fields such as mathematics and statistics Experience or strong interest in
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fields: Robotics Computer Science Electrical and Computer Engineering Mechanical Engineering Applied Mathematics Applied Physics Statistics and Optimization A strong background in robotics, machine
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, statistics, chemistry or a related area, with extensive research experience and a strong publication record. Excellent mathematical and programming skills are essential, with experience in two or more of
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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extraction techniques. Strong knowledge of protein science and its application in food systems. Proficiency in analytical techniques relevant to food and protein characterization. Skills in statistical