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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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technologies including thermal energy storage (TES) and hydrogen technologies integrating with concentrating solar power (CSP). Responsibilities under this position include leading research work on modeling
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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. Applicant must have (or expect to obtain shortly) a Ph.D. in Biological Sciences or related field. Experience with Neuroimmunology and/or Immunology is highly preferred. Proficiency in mouse models of disease
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statistical analyses on large databases Designing and running experiments Structural modelling and related econometrics Proving theoretical results Basic Qualifications Bachelor’s degree by start date (required
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the position, the required skillset includes: Strong background in electromagnetic and optical modeling using tools including Ansys, Comsol, Matlab, and other methods. Expertise in wet chemical synthesis
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of the immune system in retinal degenerative disease. We utilize induced pluripotent stem cells, human donor tissue and mouse models of retinal degeneration to study the role of monocyte derived macrophages in
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. Collectively, the team holds expertise in the clinical management and treatment of IAs, biology of the IA wall, patient-specific CFD modeling, and biomechanics of IA walls. The faculty member will contribute
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, metabolomics, clinical samples, and animal models to accomplish these goals. The results will lead to the development of novel therapeutic strategies for the clinically relevant molecular subsets of lung cancer