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analysing large and complex datasets, including multi-omics and perturbation-response data, with a strong emphasis on data quality, reproducibility, and biological interpretability. Practical experience in
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dealing with the large number of plankton types and with unresolved ecological processes especially around mortality. Modern, GPU based, modelling infrastructures provide promise towards running models
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communication skills, and can work both independently and as an effective member of a large research team. The candidate will be expected to initiate new research proposals for both intramural and extramural
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experiments within the Pediatric Brain Tumor Research Program. This position will be responsible for evaluation and analyzing large sequencing and multiome data-sets. Baylor College of Medicine typically
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description. The applicant must have excellent professional proficiency in English (written and spoken). Required: Strong knowledge of how to use AI and large language models (LLMs) for data extraction
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About the Role This is an exciting position where applicants are invited to join a multi-disciplinary team of bioengineers, biomedical scientists, and computer scientists working together at Queen
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or Functional ultrasound imaging or Electrophysiology (neuropixels) in behaving animals Quantitative data analysis and computational modeling of network activity Data acquisition systems, signal processing and
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interactions. We are looking for an enthusiastic and versatile postdoctoral researcher to apply existing bioinformatic methods and develop new pipelines to analyze and interpret high-throughput sequencing data
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of LHC data, based on solid C++ and Python expertise, is strongly appreciated. Ability to work in a large international collaboration is a must. Fluency in English, speaking and writing (B2 minimum) is
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch