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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual
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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data
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in deep learning, artificial intelligence, and machine learning methodologies (Preferred) Familiarity with Python programming (Preferred). We regret that only shortlisted candidates will be notified
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testing methodologies for physical modelling of deep-water offshore wind turbines. You should hold a relevant PhD (or be near completion) and have a strong publication history. A strong background in
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optical simulation skills (Preferably in Zemax) Strong programming skills (preferably in Python) Experience in deep learning algorithms is a plus Ability to work in a highly international team and
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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning
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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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: Job Requirements: Required: PhD in computer science, biomedical informatics, electrical engineering, or a related field Demonstrated expertise in machine learning, deep learning, and image-based