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Professor of the Practice

Below you will find the details for the position including any supplementary documentation and questions you should review before applying for the opening.  To apply for the job, please click the Apply for this Job link.

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Northeastern University Employee Benefits

Northeastern University is an Equal Opportunity, Affirmative Action Educational Institution and Employer, Title IX University. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by the law. Northeastern University is an E-Verify Employer.

Posting Details

Posting Detail Information

Position Title Professor of the Practice
Requisition Number FTFR000616
Division/College College of Computer and Information Science
Interdisciplinary Division/College
Location Boston Main Campus
Full-time/Part-time Full Time
Benefits Eligible Yes
Tenure Status Non Tenure Track
Posting Date 05/25/2016
Responsibilities

The College of Computer and Information Science (CCIS) at Northeastern University is actively looking for one or more experts in MapReduce, Distributed Computing and Data Mining/Machine Learning. The ideal candidate loves to teach and has hands on industry experience. We will consider both full- and part-time applicants at the level of Lecturer or Professor of the Practice depending on the level of industry experience. This opportunity is offered in one of three possible locations: Boston, Seattle and the Bay Area.

Qualifications

PhD in the field of Computer Science is required.

Additional Information

Below are descriptions of our current renditions of the courses:

Map Reduce – The course covers techniques for analyzing very large data sets. We introduce the MapReduce programming model and the core technologies it relies on in practice, such as a distributed file system. Related approaches and technologies from distributed databases and Cloud Computing will also be introduced. Particular emphasis is placed on practical examples and hands-on programming experience. Both plain MapReduce and database-inspired advanced programming models running on top of a MapReduce infrastructure will be used.

Machine Learning – Provides a broad look at a variety of techniques used in machine learning and data mining, and also examines issues associated with their use. Topics include algorithms for supervised learning including decision tree induction, artificial neural networks, instance-based learning, probabilistic methods, and support vector machines; unsupervised learning; and reinforcement learning. Also covers computational learning theory and other methods for analyzing and measuring the performance of learning algorithms. Course work includes a programming term project.

Distributed Systems – In today’s increasingly connected world, distributed systems permeate our daily lives. This course would cover fundamental principles and theory of distributed systems, and would discuss the design and implementation of systems from industry that incorporate them. This course will be hands-on with projects based on real-world systems. Key topics include understanding and managing concurrency, consistency/consensus, availability, partition/fault tolerance, time and logical clocks, scalability/performance, and security. We will discuss design and implementation concepts that include distributed file systems (e.g. NFS, HDFS), caches and distributed hash tables (e.g.Dynamo, Cassandra), distributed computing frameworks (Map/Reduce, Spark), overlay networks (Bittorrent, BitCoin) and content delivery networks (Akamai, Limelight), data center architectures and protocols. Students will learn about these concepts and practices both from textbook/lecture material and hands-on experience through projects that include building and deploying working distributed systems.

Documents Needed to Apply

Required Documents
  1. Curriculum Vitae
  2. Cover Letter
Optional Documents

Posting Specific Questions

Required fields are indicated with an asterisk (*).