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 for Spring term will be received until May 1, 2023. Applications for Fall/Winter term will be received until June 15, 2023. 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 on our homepage.
Courses available are listed below.
Applying as a Term Adjunct.
Applying as a Teaching Fellow.
Academic Year 2023/2024 Spring Term
This spring term period is from May 1, 2023 to June 30, 2023.
Applications for Spring term will be received until May 1, 2023.
BMIF 801 – Programming Skills and Tools for Processing Biomedical Data (3.00 units)
The objective of this course is to provide graduating health science students hands-on training in computer programming languages and tools to familiarize them with the principles and practice of cutting edge technologies for bioinformatics used in biomedical and molecular sciences research.
Prerequisite: none
Academic Year 2023/2024 Fall Term
This fall term period is from September 1, 2023 to December 31, 2023.
Applications for Fall/Winter term will be received until June 15, 2023.
CISC 235 – Data Structures (3.00 units)
Design and implementation of advanced data structures and related algorithms, including correctness and complexity analysis.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Prerequisites: Level 2 or above and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (CISC 124 and CISC 203).
CISC 320 – Fundamentals of Software Development (3.00 units)
Introduction to management of small and medium-scale software projects. Advanced programming methodology using the programming language C++. Includes a significant programming project.
Learning Hours: 120 (36 Lecture, 24 Tutorial, 24 Group Learning, 36 Private Study)
Prerequisites: Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in CISC 235.
CISC 324 – Operating Systems (3.00 units)
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 (36 Lecture, 84 Private Study)
Prerequisites: Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (CISC 221 and CISC 235).
CISC 327 – Software Quality Assurance (3.00 units)
Validation of software throughout the life cycle. Comparative effectiveness in defect removal of formal methods (proofs of correctness), inspection (walkthroughs and reviews), and testing (unit, integration, and system testing; white box versus black box).
Learning Hours: 120 (36 Lecture, 84 Group Learning)
Prerequisites: C- (or P in Winter 2020) in (CISC 220 and CISC 124) and registration in a School of Computing Plan.
Exclusion: SOFT 327
CISC 452 – Neural and Genetic Computing (3.00 units)
Introduction to neural and genetic computing. Topics include associative memory systems, neural optimization strategies, supervised and unsupervised classification networks, genetic algorithms, genetic and evolutionary programming. Applications are examined, and the relation to biologic systems is discussed.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Prerequisites: Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in CISC 235.
Exclusion: COGS 400.
CISC 471 – Computational Biology (3.00 units)
Advanced computational approaches to the problems in molecular biology. Techniques and algorithms for sequence analysis and alignment; molecular databases; protein structure prediction and molecular data mining.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Prerequisites: Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (CISC 352 and CISC 365).
CISC 486 – Game Development (3.00 units)
An introduction to ‘engines’ used in networked 3-dimensional games. Topics include game-engine architecture and components providing 3-dimensional rendering, physics simulation, sound, artificial intelligence and networking services.
Learning Hours: 120 (36 Lecture, 15 Group Learning, 69 Private Study)
Prerequisites: Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (CISC 226 and [CISC 322 or CISC 326] and CISC 324 and [MATH 110 or MATH 111 or MATH 112]).
CISC 498 – Information Technology Project (6.00 units)
Topic selected under the supervision of a faculty member. Emphasis is on the application of software engineering techniques to the development of a substantial software system. Group work, oral presentation, participation in design and code review meetings, and delivery of complete software specification and design are required.
Learning Hours: 258 (18 Seminar, 240 Group Learning)
Prerequisites: Level 4 or above and registration in a SODE Specialization Plan and a cumulative GPA of 1.90 and a (GPA of 2.60 in CISC; COCA; COGS; SOFT) and (30.0 units in CISC; COCA; COGS; SOFT) and (a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in [CISC 322 or CISC 326] and [CISC 325 or CISC 327]).
Exclusion: CISC 496; CISC 499.
COGS 100 – Introduction to Cognitive Science (3.00 units)
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.
Notes: Also offered online. Consult Arts and Science Online. Learning Hours may vary.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Prerequisites: None.
Academic Year 2023/2024 Winter Term
This winter term period is from January 1, 2024 to April 30, 2024.
Applications for Fall/Winter term will be received until June 15, 2023.
CISC 101 – Elements of Computing Science (3.00 units)
Introduction to algorithms: their definition, design, coding, and execution on computers. Intended for students who have no programming experience. All or most assignment work will be completed during lab time.
Notes: Also offered online. Consult Arts and Science Online. Learning Hours may vary.
Sufficient preparation for CISC 121; alternative to CISC 110 and CISC 151. This course is intended for students who have no programming experience.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Prerequisites: None.
Exclusion: APSC 142; APSC 143; CISC 110; CISC 151.
One-Way Exclusion: May not be taken with or after CISC 121; CISC/CMPE/COCA/COGS/SOFT at the 200-level or above.
CISC 151 – Elements of Computing with Data Analytics (3.00 units)
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.
Notes: Sufficient preparation for CISC 121; alternative to CISC 101 and CISC 110.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Prerequisites: None.
Exclusion: APSC 142; APSC 143; CISC 101; CISC 110.
One-Way Exclusion: May not be taken with or after CISC 121; CISC/CMPE/COCA/COGS/SOFT at the 200-level or above.
CISC 226 – Game Design (3.00 units)
An introduction to techniques for designing elementary computer games. Topics will include game development tools and processes, principles of game design, game prototyping and game evaluation.
Learning Hours: 120 (36 Lecture, 60 Group Learning, 24 Private Study)
Prerequisites: Level 2 or above and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in CISC 124.
CISC 235 – Data Structures (3.00 units)
Design and implementation of advanced data structures and related algorithms, including correctness and complexity analysis.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Prerequisites: Level 2 or above and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (CISC 124 and CISC 203).
CISC 324 – Operating Systems (3.00 units)
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 (36 Lecture, 84 Private Study)
Prerequisites: Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (CISC 221 and CISC 235).
CISC 423 – Software Requirements (3.00 units)
An integrated approach to discovering and documenting software requirements. Identification of stakeholders; customer, operator, analyst, and developer perspectives. Requirements elicitation. Transition from initial (informal) requirements to semi-formal and formal representations. Requirements analysis process; analysis patterns. Requirements specification techniques. Relation to architecture and user interface design; traceability of requirements.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Prerequisites: Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (CISC 223 and CISC 235).
Corequisites: (CISC 325 and [CISC 322 or CISC 326]).
CISC 451 – Topics in Data Analytics (3.00 units)
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 (36 Individual Instruction, 36 Laboratory, 48 Private Study)
Prerequisites: A minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (CISC 333 or CISC 351 or CISC 372).
CISC 458 – Programming Language Processors (S) (3.00 units)
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 (36 Lecture, 36 Laboratory, 48 Group Learning)
Prerequisites: Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (CISC 121 and CISC 221 and CISC 223).
CISC 495 – Software Evolution (3.00 units)
COGS 100 – Introduction to Cognitive Science (3.00 units)
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.
Notes: Also offered online. Consult Arts and Science Online. Learning Hours may vary.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Prerequisites: None.
COGS 201 – Cognition and Computation (3.00 units)
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 (36 Lecture, 84 Private Study)
Prerequisites: Level 2 or above and a minimum grade of a C- (obtained in any term) or a ‘Pass’ (obtained in Winter 2020) in (COGS 100 or PSYC 100).
Exclusion: COGS 200; PSYC 220.
