Computer Science & Engineering:Natural Language Processing Asst and Assoc/Full Prof University of California, Santa Cruz, Engineering School - Computer Science and Engineering United States

Computer Science & Engineering:Natural Language Processing Asst and Assoc/Full Prof (open until filled, initial review 12/13/18)


  • Engineering School - Computer Science and Engineering



Open date: October 3rd, 2018
Last review date: December 13th, 2018

Applications received after this date will be reviewed by the search committee if the position has not yet been filled.
Final date: June 30th, 2019

Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.


The Department of Computer Science and Engineering at the University of California, Santa Cruz invites applications for two positions in the field of Natural Language Processing. One position is at the tenured Associate or early stage Full Professor level, and the other position is at the tenure track Assistant Professor level. We seek outstanding applicants with research and teaching expertise in all areas of Natural Language Processing. We are especially interested in candidates who have contributed to one or more application areas of Natural Language Processing including but not limited to information extraction, dialogue systems, semantic parsing, sentiment analysis, question answering, and machine translation.


Both positions are associated with a proposed Professional MS program in Natural Language Processing to be located in the UCSC Silicon Valley Campus in Santa Clara, California. The successful candidates will play an essential role in developing, growing, and shaping this new program. They are expected to develop a research program, advise Ph.D. students in their research area, obtain external funding, develop and teach courses within the undergraduate and graduate curriculum, perform university, public, and professional service, and interact broadly with the large number of Natural Language Processing practitioners in Silicon Valley industrial research and advanced development labs. The successful candidates should be able to work with students, faculty and staff from a wide range of social and cultural backgrounds. In addition to the basic qualifications, applicants at the Associate or Full Professor level should have a demonstrated record of publications, demonstrated experience in university teaching at the undergraduate and graduate level or closely analogous activities, demonstrated record of extramural funding or similar success with garnering support for research endeavors, experience with research project management, and professional service; we also value industrial experience, and a track record of building product and applications based on NLP technology.


We welcome candidates who understand the barriers facing women and minorities who are underrepresented in higher education careers (as evidenced by life experiences and educational background), and who have experience in equity and diversity with respect to teaching, mentoring, research, life experiences, or service towards building an equitable and diverse scholarly environment.


The primary offices for these positions are located in Santa Clara, due to the expectation of teaching and mentoring students in this location. Space for PhD students for these positions is also located in Santa Clara. Graduate level teaching duties will be mainly at the Santa Clara campus with undergraduate courses to be taught at the Santa Cruz campus. The successful applicants will typically spend multiple days per week in Santa Clara and are also expected to spend on average one day per week on the Santa Cruz campus (more when teaching an undergraduate class on the Santa Cruz campus). The ability for on-demand transportation between Santa Clara and Santa Cruz with or without accommodations is essential.


The Computer Science and Engineering Department has nationally and internationally known research groups in Machine Learning, Data Science, Natural Language Processing and related fields. Our beautiful campus has a long history of embracing groundbreaking interdisciplinary work, and the proximity of the campus to Silicon Valley affords our faculty extensive opportunities for interactions and collaborations with industry.


Assistant Professor and Associate or early stage Full Professor


Commensurate with qualifications and experience; academic year (9-month basis).


A Ph.D. or equivalent foreign degree in Computer Science or a relevant field expected to be completed by June 30, 2019; demonstrated record of research and teaching.


July 1, 2019 (with academic year beginning September 2019). Degree must be in hand by June 30, 2019.


Applications are accepted via the UCSC Academic Recruit online system; all documents and materials must be submitted as PDFs.


Please refer to Position # JPF00657-19 in all correspondence.



  • Letter of application that briefly summarizes your qualifications and interest in the position
  • Curriculum vitae
  • Statement addressing contributions to diversity through research, teaching, and/or service (required). Guidelines on diversity statements can be viewed at
  • Statement of research plans
  • Statement of teaching interests and experience
  • 3–4 selected publications
  • 3 confidential letters of recommendation*


Please note that your references, or dossier service, will submit their confidential letters directly to the UC Recruit System.


*All letters will be treated as confidential per University of California policy and California state law. For any reference letter provided via a third party (i.e., dossier service, career center), direct the author to UCSC’s confidentiality statement at


Full consideration will be given to applications completed by December 13th, 2018. Applications received after this date will be considered only if the position has not been filled.




Below please find the proposed curriculum for the NLP MS degree that the advertised positions are intended to support, in case it is of use to applicants for their teaching statement.


NLP PDST Proposed Curriculum
Core courses


Core courses aim to provide basic training on the different skill sets that form the basis of Natural Language Processing as a novel discipline: algorithms for processing natural language data to derive different representations of the data, data collection and data wrangling, in particular the collection of natural language data from social media, machine learning methods for NLP, and applications. As listed in Section 2, there are five required core courses for the NLP M.S. degree, all of which will use the new NLP subject code. All of these new courses will be created from scratch and will have their enrollment restricted to students in the NLP M.S. Degree. The list of topics in each course below is just a guideline. New faculty will contribute strongly to the final definition of these courses.


NLP 201: Natural Language Processing I (5 credits). This is the first in a series of three courses on Natural Language Processing core problems and methods. Topics covered include:


What is Natural Language Processing?

Accessing text corpora and lexical resources
Processing raw text
Categorizing and tagging words
Extracting information from text
Analyzing sentence structure
Analyzing the meaning of sentences
Ranges of applications such as question answering, dialogue systems, sentiment analysis, information extraction, knowledge extraction.


NLP 202: Natural Language Processing II (5 credits). This is the second in a series of three core courses. Topics covered include:

Morphological Processing: What are words made of, how languages differ
Lexical Processing: Lexical representations and theories
Online Lexicons and Tools: Wordnet, Verbnet
Word Embeddings & How to create them
Dependency syntax and constituent structure
Building feature based grammars
Syntactic processing: Parsing & Chunking


NLP 203: Natural Language Processing III (5 credits). This is the third in a series of three core courses. Topics covered include:

Semantic Representations
Logical representations
Distributional Semantics
Tools for semantic representation and processing
Semantic Role Labelling
Google Knowledge Graph
Theories and Representations of Discourse & Pragmatics
Coreference resolution
Dialogue modeling
Discourse relations


NLP 220: Tools of the NLP trade: Data Collection, Wrangling and Crowdsourcing (5 credits). This class focuses on getting, cleaning and working with NLP data and resources. Topics covered include:

Scraping Data
Social Media APIs, Twitter, Facebook
Crowdsourcing methods
Annotation Reliability
Data Integration and Storage
Databases for NLP data


NLP 242: Machine Learning for Natural Language Processing (5 credits). This class focuses on modern machine learning methods and their use in Natural Language Processing. Topics covered include:

Machine Learning models and Tools
Representations of Language data for Machine Learning: Features, Embeddings
Text Classification
Probabilistic Soft Logic
HMMs, Sequential Models
Evaluation methods, measures


NLP 280: Seminar in Natural Language Processing (2 credits). Students attend biweekly talks by industry and academics working on NLP methods and applications.


At steady state we expect to have 50 students in the program, thus no required course will have more than 50 students.


The courses will be focused on the application of methods to realistic situations and evaluation will emphasize individual and group projects motivated by the interests of the students. Although the projects for each class may be independent from each other, students will be encouraged to think about project topics during their first quarter (e.g., for NLP 201 and NLP 242) that they can continue to develop as they progress through NLP 202, their electives, and NLP 271A and B.


Elective courses
Electives focus either on deep knowledge of methods or theory or specific application areas. The expertise gained through the electives are applicable to a wide variety of industries and tasks. Planned electives for the program include the following, but only NLP 260, NLP 245 and NL 243 are in the provisional CLP for the first year.


This might change depending on various factors, such as the new faculty hired for the NLP MS. Some electives will be taught by industry practitioners hired as lecturers or ladder-rank faculty. Electives will be taught to a mix of NLP M.S. students as well as Ph.D. students from other programs.


NLP 260: Information Extraction (5 credits).
Methods in Information Extraction
Common semantic web sources (dbpedia,, etc)
Data Cleaning
Extracting Entities and Relations from Text
Ontology Based Extraction and Schema mapping
Applications: Finance, Law, Ontology Learning etc


NLP 265: Sentiment Analysis (5 credits).
What is Sentiment Analysis and how is it used?
Methods in Sentiment Analysis
Applications of Sentiment Analysis
Off-the-Shelf tools and hands-on use
Sentiment Analysis beyond the sentence


NLP 245: Conversational Agents (5 credits).
What are Conversational Agents and what are their applications?
Theoretical and Computational Models of Conversation
Representations of Discourse, Discourse Context, User Models
Task-Oriented Conversational Agents
Question Answering and Search
Open-Domain Conversational Agents


NLP 270: Linguistic Models of Syntax and Semantics for Computer Scientists (5 credits).
The distributional method in syntax; lexical categories and lexical semantics
Phrase structure grammars and feature unification grammars
Universal Treebank
Argument structure and thematic roles
Compositional models of interpretation
Speech acts and non-truth conditional meaning
Intonational meaning and discourse structure


NLP 243: Deep Learning for NLP (5 credits).
How is Deep Learning specialized for NLP
Word Vectors: Distributed representations of Words and Phrases
Word Vectors: Specialized representations and the evaluation of embeddings
Neural Networks: DNNs and their variants, Back-propagation, Tensor Flow
Recurrent Neural Networks and Language Models
Differences between Parsing, Translation, and Dialogue
Attention and Transformer Models
Semi-Supervised Learning, Reinforcement Learning, Multi-Task Learning


UC Santa Cruz faculty make significant contributions to the body of research that has earned the University of California the ranking as the foremost public higher education institution in the world. In the process, our faculty demonstrate that cutting-edge research, excellent teaching and outstanding service are mutually supportive.


The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. UC Santa Cruz is committed to excellence through diversity and strives to establish a climate that welcomes, celebrates, and promotes respect for the contributions of all students and employees. Inquiries regarding the University’s equal employment opportunity policies may be directed to the Office for Diversity, Equity, and Inclusion at the University of California, Santa Cruz, CA 95064 or by phone at (831) 459-2686.


Under Federal law, the University of California may employ only individuals who are legally able to work in the United States as established by providing documents as specified in the Immigration Reform and Control Act of 1986. Certain UCSC positions funded by federal contracts or sub-contracts require the selected candidate to pass an E-Verify check (see More information is available at the APO website (see or call (831) 459-4300.


UCSC is a smoke & tobacco-free campus.


If you need accommodation due to a disability, please contact the Academic Personnel Office at (831) 459-4300. 



Santa Cruz, CA



Document requirements
  • Cover Letter - Letter of application that briefly summarizes your qualifications and interest in the position

  • Curriculum Vitae - Your most recently updated C.V.

  • Statement of Contributions to Diversity - Statement addressing past and/or potential contributions to diversity through research, teaching, and/or service.

  • Statement of Research Plans

  • Statement of Teaching Interests and Experience

  • Select Publication

  • Select Publication

  • Select Publication

  • Select Publication (Optional)

Reference requirements
  • 3 letters of reference required



  1. Create an ApplicantID

  2. Provide required information and documents

  3. If any, provide required reference information

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