Teaching Positions Available for the School of Computing – 2019-20 Academic Year

The School of Computing at Queen’s University invites applications from suitably qualified candidates interested in teaching courses shown below.

The University invites applications from all qualified individuals. Queen’s University is committed to employment equity and diversity in the workplace and welcomes applications from women, visible minorities, aboriginal people, persons with disabilities, and persons of any sexual orientation or gender identity. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.

Applications will be received until March 15, 2019. Review of applications will commence shortly thereafter, and the final appointment is subject to budgetary approval. Additional information about the School of Computing can be found at http://www.queensu.ca

Courses available are listed below.

To apply as a Term Adjunct, see: http://flux.cs.queensu.ca/employment/applying-for-a-term-adjunct-position/

To apply as a Teaching Fellow, see: http://flux.cs.queensu.ca/employment/applying-for-a-teaching-follow-position/

 



Academic Year 2019/2020 Spring Term

This fall term period is from May 1, 2019 to June 30, 2019.

Classes will be in session from May 6, 2019 to June 17, 2019.

Applications will be received until March 15, 2019.



COGS 100/3.0 Introduction to Cognitive Science
A multidisciplinary approach to the study of the mind combining approached from  philosophy, psychology, linguistics, neuroscience, anthropology, and artificial intelligence. Logic, rules, concepts, and other mental representations used to generate thought and behaviour. Implementation of computational and cognitive models of mental processes.
NOTE Also offered online. Consult Arts and Science Online. Learning Hours may vary.
LEARNING HOURS 120 (36L;84P)
ONE-WAY EXCLUSION May not be taken with or after CISC 352/3.0; PSYC 200/6.0.


BMIF-802/3.0 Biomedical Data Analysis
Provides students with hands-on training in analysis of biomedical datasets. Topics  will include feature extraction and classification, pattern recognition, supervised and unsupervised learning, and basic concepts of biostatistics as applied to the analysis of biomedical data. Examples of real biomedical datasets will be provided to demonstrate various methodologies for data analysis. Prerequisites for this course include BMIF 801 as well as admission in the Graduate Diploma (GDip [BI]) or permission of the Course Instructor.

 



Academic Year 2019/2020 Summer Term

This fall term period is from July 1, 2019 to August 31, 2019.

Classes will be in session from July 2, 2019 to August 12, 2019.

Applications will be received until March 15, 2019.



CISC 121/3.0 Introduction to Computing Science I
Introduction to design and analysis of algorithms. Recursion, backtracking, and exits. Sequences, linked lists and references. Binary search trees. Elementary searching and sorting. Assertions and loop invariants. Order-of-magnitude complexity. Numerical computation. Documentation, testing and debugging.NOTE Also offered online. Consult Arts and Science Online. Learning Hours may vary.
LEARNING HOURS 120 (36L;84P)
RECOMMENDATION Some programming experience (such as high-school level programming or CISC 101/3.0 or CISC 110/3.0); see Introductory Courses in Departmental Notes.
COREQUISITE CISC 102/3.0 or MATH 110/6.0 or MATH 111/6.0 or MATH 112/3.0 or MATH 120/6.0 or MATH 121/6.0 or MATH 123/3.0 or MATH 124/3.0 or MATH 126/6.0 or APSC 171/3.0 or APSC 172/3.0 or APSC 174/3.0 or COMM 161/3.0 or COMM 162/3.0.


CISC 124/3.0 Introduction to Computing Science II
Introduction to object-oriented design, architecture, and programming. Use of packages, class libraries, and interfaces. Encapsulation and representational abstraction. Inheritance. Polymorphic programming. Exception handling. Iterators. Introduction to a class design notation. Applications in various areas.
LEARNING HOURS 120 (36L;24Lb;60P)
PREREQUISITE C- in CISC 121/3.0.
COREQUISITE CISC 102/3.0 or MATH 110/6.0 or MATH 111/6.0 or MATH 112/3.0 or MATH 120/6.0 or MATH 121/6.0 or MATH 123/3.0 or MATH 124/3.0 or MATH 126/6.0 or APSC 171/3.0 or APSC 172/3.0 or APSC 174/3.0 or COMM 161/3.0 or COMM 162/3.0.


CISC-897/3.0 Research Methods in Computer Science     
This course provides an introduction to the primary and secondary sources of information in the computing science literature. The course includes work aimed at improving research skills. Students are required to submit and present a paper on a topic that relates to their research.
Prerequisite: none

 



Academic Year 2019/2020 Fall Term

This fall term period is from September 1, 2019 to December 31, 2019.

Classes will be in session from September 5, 2019 to November 29, 2019.

Applications will be received until March 15, 2019.



CISC 124/3.0 Introduction to Computing Science II
Introduction to object-oriented design, architecture, and programming. Use of packages, class libraries, and interfaces. Encapsulation and representational abstraction. Inheritance. Polymorphic programming. Exception handling. Iterators. Introduction to a class design notation. Applications in various areas.
LEARNING HOURS 120 (36L;24Lb;60P)
PREREQUISITE C- in CISC 121/3.0.
COREQUISITE CISC 102/3.0 or MATH 110/6.0 or MATH 111/6.0 or MATH 112/3.0 or MATH 120/6.0 or MATH 121/6.0 or MATH 123/3.0 or MATH 124/3.0 or MATH 126/6.0 or APSC 171/3.0 or APSC 172/3.0 or APSC 174/3.0 or COMM 161/3.0 or COMM 162/3.0.


CISC 181/3.0 Digital Societies
This introductory course provides a broad overview and ethical implications of technological topics and trends in the digital world such as the Internet of Things (IoT), Social Networks, Security and Privacy, Data Analytics, and Artificial Intelligence (AI). No programming experience is required.
LEARNING HOURS 120 (36L; 84P)


CISC 322/3.0 Software Architecture
Abstractions and patterns of interactions and relationships among modules. Design recovery; relationship of architecture to requirements and testing.LEARNING HOURS 120 (36L;24T;36G;24P)
PREREQUISITE Registration in a School of Computing Plan and C- in (CISC 203/3.0 and CISC 204/3.0 and CISC 223/3.0 and CISC 235/3.0).
EXCLUSION No more than 3.0 units from CISC 322/3.0 and CISC 326/3.0.


CISC 330/3.0Computer-Integrated Surgery
Concepts of computer-integrated surgery systems and underlying techniques such as medical-image computing, robotics, and virtual reality, learned through real-life applications and problems. Techniques learned in class will be applied in a hands-on surgery session where students perform minimally invasive surgery with virtual-reality navigation tools.
LEARNING HOURS 120 (36L;84P)
PREREQUISITE Registration in a School of Computing Plan and C- in (CISC 121/3.0 and CISC 271/3.0).
EXCLUSION No more than 3.0 units from CISC 330/3.0; COMP 329/3.0; COMP 230/3.0.
EQUIVALENCY COMP 230/3.0.


CISC 365/3.0 Algorithms I
Principles of design, analysis and implementation of efficient algorithms. Case studies from a variety of areas illustrate divide and conquer methods, the greedy approach, branch and bound algorithms and dynamic programming.
LEARNING HOURS 120 (36L;84P)
PREREQUISITE Registration in a School of Computing Plan and C- in (CISC 203/3.0 and CISC 204/3.0 and CISC 235/3.0).


CISC 435/3.0 Computer Communications and Networks
Fundamental concepts in the design and implementation of computer communication networks, protocols, and applications. Overview of network architectures; applications; network programming interfaces (e.g., sockets); transport; congestion; routing and data link protocols; addressing; local area networks; wireless networks, mobility management; security.
LEARNING HOURS 120 (36L;84P)
PREREQUISITE Registration in a School of Computing Plan and C- in CISC 324/3.0.


CISC 451/3.0 Topics in Data Analytics
Content will vary from year to year; typical areas covered may include: tools for large scale data analytics (Hadoop, Spark), data analytics in the cloud, properties of large scale social networks, applications of data analytics in security.
LEARNING HOURS 120 (36L;84P)
PREREQUISITE C- in (CISC 333/3.0 or CISC 351/3.0).


CISC 474/3.0 Reinforcement Learning
This course includes topics on formal and heuristic approaches to problem-solving, planning, reinforcement learning, knowledge representation and reasoning, Markov decision processes, dynamic programming, temporal-difference learning, Monte Carlo reinforcement learning methods, function approximation methods, integration of learning and planning. Students will implement simple examples of logical reasoning, clustering and classification.
LEARNING HOURS 120 (36L;84P)
PREREQUISITE  CISC 352; programming expertise
EXCLUSION CISC 453*/3.0


COGS 201/3.0 Cognition and Computation
Introduction to the computational aspects of the mind. Implementation of computer programs for reasoning, decision making, and problem solving to understand these mental processes. Information theory and behaviourism; computational models of cognition, perception and memory processes demonstrating modeling approaches, and cognitive architectures.
LEARNING HOURS 120 (36L;84P)
PREREQUISITE Level 2 or above and C- in (COGS 100/3.0 or PSYC 100/6.0).
EXCLUSION No more than 6.0 units from COGS 200/6.0; COGS 201/3.0; PSYC 220/6.0.

 



Academic Year 2019/2020 Winter Term

This fall term period is from January 1, 2019 to April 30, 2019.

Classes will be in session from January 6, 2019 to April 3, 2019.

Applications will be received until March 15, 2019.



CISC 121/3.0Introduction to Computing Science I
Introduction to design and analysis of algorithms. Recursion, backtracking, and exits. Sequences, linked lists and references. Binary search trees. Elementary searching and sorting. Assertions and loop invariants. Order-of-magnitude complexity. Numerical computation. Documentation, testing and debugging.
NOTE Also offered online. Consult Arts and Science Online. Learning Hours may vary.
LEARNING HOURS 120 (36L;84P)
RECOMMENDATION Some programming experience (such as high-school level programming or CISC 101/3.0 or CISC 110/3.0); see Introductory Courses in Departmental Notes.
COREQUISITE CISC 102/3.0 or MATH 110/6.0 or MATH 111/6.0 or MATH 112/3.0 or MATH 120/6.0 or MATH 121/6.0 or MATH 123/3.0 or MATH 124/3.0 or MATH 126/6.0 or APSC 171/3.0 or APSC 172/3.0 or APSC 174/3.0 or COMM 161/3.0 or COMM 162/3.0.


CISC 151/3.0 Elements of Computing with Data Analytics
Introduction to algorithms: their definition, design, coding, and execution on computers, with applications drawn from data analytics, including simple prediction and clustering. Intended for students who have no programming experience. All or most assignment work will be completed during lab time.
LEARNING HOURS 120 (36L;36Lb;48P)
EXCLUSION No more than 3.0 units from APSC 142/3.0; APSC 143/3.0; CISC 101/3.0; CISC 110/3.0; CISC 151/3.0.
ONE-WAY EXCLUSION May not be taken with or after CISC 121/3.0; CISC; SOFT at the 200-level and above.


CISC 181/3.0 Digital Societies
This introductory course provides a broad overview and ethical implications of technological topics and trends in the digital world such as the Internet of Things (IoT), Social Networks, Security and Privacy, Data Analytics, and Artificial Intelligence (AI). No programming experience is required.
LEARNING HOURS 120 (36L; 84P)


CISC 223/3.0 Software Specifications
Introduction to techniques for specifying the behaviour of software, with applications of these techniques to design, verification and construction of software. Logic-based techniques such as loop invariants and class invariants. Automata and grammar-based techniques, with applications to scanners, parsers, user-interface dialogs and embedded systems. Computability issues in software specifications.
LEARNING HOURS 120 (36L;84P)
PREREQUISITE Level 2 or above and C- in CISC 124/3.0 and C- in (CISC 102/3.0 or MATH 110/6.0).
COREQUISITE CISC 204/3.0.


CISC 324/3.0 Operating Systems
Layered operating systems for conventional shared memory computers: concurrent processes. Synchronization and communication. Concurrent algorithms. Scheduling. Deadlock. Memory management. Protection. File systems. Device management. Typical layers.
LEARNING HOURS 120 (36L;84P)
PREREQUISITE Registration in a School of Computing Plan and C- in (CISC 221/3.0 and CISC 235/3.0).


CISC 458/3.0 Programming Language Processors
Introduction to the systematic construction of a compiler: grammars and languages, scanners, top-down and bottom-up parsing, runtime organization, symbol tables, internal representations; Polish notation, syntax trees, semantic routines, storage allocation, code generation, interpreters.
LEARNING HOURS 120 (36L;36Lb;48G)
PREREQUISITE Registration in a School of Computing Plan and C- in (CISC 121/3.0 and CISC 221/3.0 and CISC 223/3.0).


COCA 201/3.0 Introduction to Computing and the Creative Arts
A multidisciplinary studio-oriented overview of computer-based applications in Art, Music, Drama and Film. History of human-computer interaction. Critical and philosophical issues. Animation. Virtual reality. Computer-aided design. Computer games. Enrolment is limited.
LEARNING HOURS 120 (36L;84P)
PREREQUISITE Level 2 or above and (C- in 6.0 units in ARTF or ARTH or DRAM or FILM or MUSC at the 100-level.
COREQUISITE CISC 101/3.0 or CISC 110/3.0 or CISC 121/3.0 or CISC 151 or permission of the School of Computing.


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Teaching Positions Available for the School of Computing – 2018-19 Academic Year

The School of Computing at Queen’s University invites applications from suitably qualified candidates interested in teaching courses shown below.

The University invites applications from all qualified individuals. Queen’s University is committed to employment equity and diversity in the workplace and welcomes applications from women, visible minorities, aboriginal people, persons with disabilities, and persons of any sexual orientation or gender identity. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.

Applications will be received until November 30, 2018. Review of applications will commence shortly thereafter, and the final appointment is subject to budgetary approval. Additional information about the School of Computing can be found at http://www.queensu.ca

Courses available are listed below.

To apply as a Term Adjunct, see: http://flux.cs.queensu.ca/employment/applying-for-a-term-adjunct-position/

To apply as a Teaching Fellow, see: http://flux.cs.queensu.ca/employment/applying-for-a-teaching-follow-position/

 



Academic Year 2018/2019 Winter Term

This winter term period is from January 1, 2019 to April 30, 2019.

Classes will be in session from January 8, 2019 to April 6, 2019.

Applications will be received until November 30, 2018.



CISC 325-X Human-Computer Interaction
Developing usable software requires that human factors be considered throughout the design and development process. This course introduces a series of techniques for development and evaluating usable software, and shows how these techniques can be integrated into a process for software development.
LEARNING HOURS: 120 (36L;84P)
PREREQUISITE: Registration in a School of Computing Plan and C- in (CISC 124/3.0 and CISC 235/3.0).


COCA 201-X Introduction to Computing and the Creative Arts
A multidisciplinary studio-oriented overview of computer-based applications in Art, Music, Drama and Film. History of human-computer interaction. Critical and philosophical issues. Animation. Virtual reality. Computer-aided design. Computer games. Enrolment is limited.
LEARNING HOURS: 120 (36L;84P)
PREREQUISITE: Level 2 or above and (C- in 6.0 units in ARTF or ARTH or DRAM or FILM or MUSC at the 100-level.
COREQUISITE: CISC 101/3.0 or CISC 110/3.0 or CISC 121/3.0 or CISC 151 or permission of the School of Computing.


CISC 203-X Discrete Mathematics for Computing II
Proof methods. Combinatorics: permutations and combinations, discrete probability, recurrence relations. Graphs and trees. Boolean and abstract algebra.
LEARNING HOURS: 120 (36L;84P)
PREREQUISITE: Level 2 or above and C- in [CISC 121/3.0 and (CISC 102/3.0 or MATH 110/6.0)].


CISC 260-X Programming Paradigms
Review of imperative programming features. Introduction to other widely used programming paradigms. Functional programming languages, such as LISP and Haskell. Higher order functions, lazy evaluation, abstract and recursive types, structural induction, symbolic expressions. Logic programming languages, such as PROLOG. Operational interpretation of predicates and terms, proof search, unification, backtracking. Typical applications.
LEARNING HOURS: 120 (36L;84P)
PREREQUISITE: Level 2 or above and C- in CISC 124/3.0 and C- in (CISC 102/3.0 or MATH 110/6.0).
COREQUISITE: CISC 204/3.0.


 

Applications will be received until Nov 30, 2018.

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Teaching Assistant Positions available for 2018-2019 in the School of Computing

The Teaching Assistant Positions are available in the School of Computing for the 2018-2019 academic year.

To apply for a TA position:

  1. Go to https://auth.caslab.queensu.ca/ta. You will be prompted by the Queen’s sign-on portal for your netid and password.
  2. After logging on, provide your name and email to the system.
  3. Next, go to the “TA applications” tab at the top of the page, select “Provide background”, and provide your background information. Then click “Submit” at the bottom of the page.
  4. Finally, and only after providing your background and clicking “Submit” on that page, go to the “TA applications” tab, select “Apply”, and indicate the courses to which you are applying. Then click “Submit”.

The system will tell you when your application is complete.

Incomplete applications will not be considered.

For more information please contact:

Debby Robertson
Graduate Program Assistant
School of Computing
Queen’s University

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Post-Doctoral Fellowship in Text Data Analytics at Queen’s School of Computing (BAM Lab)

Posting Date: Mar 5, 2018 (revised – higher salary and international applicants will be considered)
Job Title: Post-Doctoral Fellow, School of Computing, Queen’s University
Funded by: IBM and CIMVHR (Canadian Institute of Military and Veteran Health Research)
Department: School of Computing, Queen’s University
Collaborators: University of Manitoba, Western University
Project Title: Defining Post Traumatic Stress Disorder (PTSD) in Primary Care Electronic Medical Record (EMR) Data to Explore Prevalence, Patient Characteristics and Primary Care Experiences of Veterans, Families of Military Service Members and the General Population
Research Areas: Natural Language Processing (NLP), text mining, machine learning, knowledge representation and management, medical decision support system
Supervision: Dr. Farhana Zulkernine and academic collaborators (primarily Dr. Alexander Singer, University of Manitoba)
Remuneration: $50,000 CDN per year (including all benefits)
Start Date: May 1st, 2018
Duration: 2 years
Application Deadline: Until hired
Application Procedure: Apply by email by sending the application package to
farhana@cs.queensu.ca.

Qualifications: We seek a candidate with proven expertise in text data mining, natural language processing (NLP), deep learning/machine learning, knowledge management using big data storage systems
and knowledge sharing using cloud services. The work will focus on using NLP and text mining techniques to extract terms that are representative of PTSD diagnosis from doctors’ chart notes stored in the EMR systems. The data must be anonymized and analyzed to extract indicators of possible development or progression towards developing PTSD and diagnosis of PTSD in different sectors such as veteran population, their families and general public to understand and evaluate quality of primary care for patients with PTSD. The extracted and analyzed data must be stored to enable knowledge translation and sharing with the research community.

A PhD in Computer Science or a comparable qualification is required. The successful candidate must have a good research record with publications in relevant international conferences and journals, text analytics, and machine learning. The candidate is expected to have an active role in various collaboration efforts with other universities and industry, both at the national and international level. Good communication and project management skills and willingness to work in a team are essential. Additional information about Dr. Zulkernine’s research and the project can be found at http://cs.queensu.ca/~farhana/.

The position is for two years and is funded by IBM and CIMVHR (Canadian Institute for Military and Veteran Health Research). The candidate will be working in the Big Data Analytics and Management laboratory (BAM Lab) at the School of Computing, Queen’s University but will be closely collaborating with Dr. Alex Singer at the University of Manitoba and other collaborators involved in this project. Periodic reports must be presented about the status and progression of the work. The candidate will also be expected to supervise other graduate and undergraduate students on relevant project and will have an opportunity to have joint publications and gather experiences in academic jobs.

The School of Computing is one of the premier computing departments in Canada with about 30 faculty members and close to 150 graduate students. Kingston is Canada’s first capital and is centrally located between Toronto, Ottawa, and Montreal. The United Nations rates Canada as one of the best countries in the world to live. The project will provide a unique opportunity to the candidate to work with multiple experts on an interdisciplinary research which will contribute to Canada’s health care system and allow exploration of cutting edge tools and techniques in text data analytics, knowledge management, and machine learning.

Please send an application package to farhana@cs.queensu.ca containing:

  1.  A cover letter explaining experiences relevant to the project.
  2. A curriculum vitae with detailed information regarding your academic degree,
    research projects and publications.
  3. Names and email addresses of three referees.
  4. Sample publications (1-2 recent publications).

***Canadian citizen/immigrant/permanent resident will be given priority.

EMPLOYMENT EQUITY: The University invites applications from all qualified candidates. Queen’s is committed to employment equity and diversity in the workplace and welcomes applications from women, visible minorities, Aboriginal peoples, persons with disabilities, and persons of any sexual orientation or gender identity.

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School of Computing Teaching Opportunities

There are no teaching positions currently available in the School of Computing.

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Postdoctoral Fellowship Opportunities

There are no positions currently available in the School of Computing.

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