|Part of our mission is to ignite and support new partnerships in Big Data that tackle societal challenges with data-driven tools and methods. We’ve shared a selection of opportunities submitted from our community which may be relevant for your work, below. Check each organization’s website for the most up-to-date eligibility criteria, application instructions, and deadlines.
If you have an opportunity you’d like to share, please email email@example.com.
NIH Data Commons Pilot Phase Research Opportunity Announcement
Date: June 23, 2017
The purpose of the announcement is to invite applications from applicants who have an interest in performing high impact, cutting-edge scientific and computing activities necessary to establish an NIH Data Commons. The goal of the NIH Data Commons is to accelerate new biomedical discoveries by providing a cloud-based platform where investigators can store, share, access, and compute on digital objects (data, software, etc.) generated from biomedical research and perform novel scientific research including hypothesis generation, discovery, and validation. Applicants are encouraged to develop innovative approaches to one or more key computational, data, analytical and scientific capabilities of the Data Commons. These key capabilities are described in detail in the announcement.
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NSF/CISE is encouraging the submission of supplemental funding requests (of up to $15,000 each, covering travel, lodging and subsistence) to strengthen and expand collaborations between NSF- and JST-funded PIs in shared priority areas. CISE anticipates awarding up to 10 such supplemental funding requests in FY 2018.
Job Posting: Full-Time Faculty, Harrisburg University
Applications accepted until filled.
HU seeks faculty members with expertise in the following areas:
Unstructured Data Analysis: Candidates in unstructured data analysis should have a solid background in natural language processing, computational linguistics, or psycholinguistics coming from either the field of artificial intelligence or cognitive science. Candidates should be able to demonstrate expertise in topic areas includes peer-reviewed publications and an interview seminar.
Machine Learning: Candidates in machine learning should be familiar with the full spectrum of machine learning topics and specialization in one or more topic areas. These topics include: artificial neural networks along with the cognitive and neuro science that supports development of intelligent agents acting in networks, genetic algorithms, and pattern recognition, as well as the basic topics in machine learning; classification, association, clustering, PCA, Bayesian Learning and Bayesian Belief Networks, etc. Demonstration of expertise in topic areas includes peer-reviewed publications and an interview seminar.
Forecasting: Candidates in the Forecasting track should have a strong background in risk modeling and assessment, optimization, quantitative decision-making as well as modeling, simulation and gamification. Demonstration of expertise includes peer-reviewed publications and an interview seminar.