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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas
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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas
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Delft explores AI-driven methods to enhance closed-loop simulation for safety-critical scenarios. A key focus is developing learned simulators that generate radar and lidar data from camera sensors
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(crowding, concentration of nutrients, size of liposome, etc.) with gene expression levels across a synthetic genome. To achieve this aim, you will use VASA-seq and Ribo-Seq to generate large data sets
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strong academic record with a solid background in Machine Learning (Deep Learning, generative models, diffusion models). Knowledge in sensor data processing and radaris a plus. Good programming skills
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Website https://www.academictransfer.com/en/jobs/350125/phd-candidate-in-causal-learnin… Requirements Specific Requirements You are/ You have: a MSc degree in statistics, machine learning, data science or a
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the gap between advanced machine learning and clinical application. Your tasks are: - Exploring molecular dynamics and proteomics methods to enhance variant interpretation, which may also include a role for
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-heuristic search algorithms such as Quantum Annealing (QA). Your contributions will be pivotal in advancing the field of DSE in the computer system domain. This PhD position offers the chance to not only push
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models and algorithms requires working at the intersection of machine learning and mathematical optimization. Evaluating such algorithms requires close interaction with our industrial project partner from
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related subject (or be close to completing the degree); demonstrated interest in AI reasoning systems, algorithms, IoT, context-aware pervasive computing, machine learning and data analysis, software