Post Doctoral Fellow (cf)

Description The Faloutsos lab at Carnegie Mellon University is seeking a highly motivated candidate for a postdoctoral fellowship to do cutting edge research at the interface of complex time-evolving graphs, financial services, and machine learning. The fellow will focus on mapping the structure and dynamics of complex financial systems, extracting nontrivial patterns, detecting anomalous behaviors, and predicting outcomes.

Responsibilities:

Develop machine learning and deep learning algorithms for fraud detection in large-scale financial transactions. Perform large scale data analytics and pattern mining on graphs Work with a hybrid environment composed of researchers, data scientists, and financial services experts Report findings in high impact peer-reviewed venue Qualifications PhD in Machine learning Experience in anomaly detection, graph mining and time series. Application Instructions Equal Employment Opportunity Statement Carnegie Mellon University is an equal opportunity employer. It does not discriminate in admission, employment, or administration of its programs or activities on the basis of race, color, national origin, sex, disability, age, sexual orientation, gender identity, pregnancy or related condition, family status, marital status, parental status, religion, ancestry, veteran status, or genetic information. Furthermore, Carnegie Mellon University does not discriminate and is required not to discriminate in violation of federal, state, or local laws or executive orders.