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Postdoc Positions in Data Science/AI for Smart Health

Description

An interdisciplinary team at UC Davis is soliciting applications for a postdoctoral fellow with a start date of July 1st, 2019, or earlier. The position is for two years (extendable to three) and spans all areas of Data Science & AI applied to smart health area. The postdoc will interface with data scientists on campus, healthcare professionals, and health/bio informatics experts to pursue innovations in health diagnosis, prognosis, & treatment for both acute and chronic diseases through the use of IoTs/smart devices, advanced analytic models, machine learning/deep learning, and AI-assisted cyber-physical systems (CPS) The initial investigation involves performing multi-modal analysis of new and existing datasets (consisting of cardiac output, respiratory rate, and mechanical ventilation waveforms) and developing predictive models to predict responsiveness to treatments. Two example collaborative projects between the Robust and Ubiquitous Networking Lab with the UC Davis School of Medicine are highlighted here:

• ICCare
• EPACC

Interested applicants should email a CV, transcript, and contact information of 2-3 references to Prof. Chen-Nee Chuah chuah@ucdavis.edu with subject title [ICCcare/EPACC]. The CV should list relevant research/projects (papers, reports, and links to relevant repositories).

Qualifications

The applicant should have earned a PhD (before June 30, 2019) in Electrical/Computer Engineering, Computer Science, Bioinformatics, Biostatistics, Statistics, Applied Math, or related fields. Desired technical skills include experience in:

• Machine learning (supervised & unsupervised learning) and statistical learning
• Reinforcement learning
• Deep learning
• Programming (python, C/C++, etc.)

Candidates should have excellent writing, communication and project management skills. This position also requires the ability to work independently and cooperate with an interdisciplinary team. Familiarity with healthcare is helpful, but not necessary, for consideration.