83 machine-learning-phd-in-denmark Postdoctoral positions at University of Washington in United-States
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, students and citizens, cultivating communities who work with and learn from each other while tackling critical environmental challenges. The School of Environmental and Forest Sciences is dedicated
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learn advanced instrumentation, 3D data analysis, and AI methods in close collaboration with engineers and physicists. We work closely with lab members to develop the skills, confidence, and creativity
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the geography of outdoor activities and psychological stress. Duties/Responsibilities The researchers will contribute specifically through: Gathering data, developing and implementing machine learning models, and
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Qualifications: Completion of a PhD in aerospace engineering, physics, or a related field at the time of the appointment. Experience in experimental plasma physics. Ability to function and thrive in a
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experiments and publish papers, under the supervision of the PI on a project in the broad area of epithelial cell mechanobiology. Mentor PhD students, assist in lab organization, and perform lab duties as
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interpreting wet-lab synthesis data are encouraged to apply and will have opportunities to explore machine learning-guided approaches in chemistry. In addition to excellent research skills, we are seeking
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, and use deep learning to gain insight into biological processes. You will also gain direct exposure to cardiovascular physiology and rodent imaging in close collaboration with biologists. We work
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for this position. More About This Job Required Qualifications: PhD in Psychology or related field. Preferred Qualifications: Experience in the fields of digital interventions for mental health and/or eating
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Required Qualifications: PhD in Psychology or related field. Preferred Qualifications: Experience in the fields of digital interventions for mental health and/or eating disorders, with publications in
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. Experience with high-throughput molecular biology assays. Experience with complex functional experiments. Background in machine learning, AI, or data integration for genomic datasets. Familiarity with gene