Fellowship Header


Dalhousie's Faculty of Computer Science offers competitive funding to qualified graduate students and is committed to promoting excellence in research and teaching.

We have a diverse group of award-winning professors working in interdisciplinary research across five core areas: Big Data Analytics, Artificial Intelligence & Machine LearningHuman-Computer Interaction, Visualization & GraphicsSystemsAlgorithms & Bioinformatics, and Computer Science Education.

We are pleased to highlight a few of this year's funded fellowship opportunities available to incoming Master of Computer Science and PhD students

Cluster Banner 3

Advanced Algorithm Design for Inferring Evolution

Phylogenetic “family trees” are a primary tool used for studying evolution such as the spread of antibiotic resistant bacteria or transmission of COVID-19. As we collect more and more sequencing data we need better algorithms to infer and compare phylogenetic trees. The successful applicant will design and implement new algorithms and data structures based on graph theory or statistical models for complex problems and big data challenges. The ideal candidate has a strong interest in algorithm design and analysis, graph theory, or bioinformatics.

Accepting: PhD students

Express your interest in working with Dr. Chris Whidden.

Evolving Threat Detector for Antimicrobial Resistance

This project will be focused on the development of computational methods for automatically identifying novel and evolving antimicrobial resistance (AMR) genes from the genomes of bacterial pathogens.  This will build on existing efforts to characterise AMR genes across massive genome databases and will involve integration, visualisation, and analysis of heterogeneous datasets including genomic sequence data, evolutionary trees,  spatiotemporal metadata, and the results of molecular diagnostic tests.

Accepting: PhD and MCS students

Express your interest in working with Dr. Finlay Maguire.

Graph-theoretic approaches to mapping antimicrobial resistance transmission

This project will combine machine learning and metagenomic sequence graph data to track and predict the evolution of antimicrobial resistance (AMR) genes between clinical, agricultural, urban, and wildlife samples.  This project will build on preliminary work identifying and filtering local sub-graphs from large complex metagenomic sequence graphs.  Using simulation and graph ML approaches, patterns of lateral gene transfer will be predicted from the local graph structure. Together, this will be used to pinpoint (and target interventions) to key loci of intersectoral AMR transmission.

Accepting: PhD students

Express your interest in working with Dr. Finlay Maguire.

Space Efficient Data Structures with Applications to Large Data

  1. Algorithms and data structures, especially fast and space-efficient algorithms and data structures, including succinct data structures, string algorithms and text indexing, I/O-efficient algorithms, implicit data structures, and adaptive algorithms.
  2. Computational Geometry, especially efficient algorithms and data structures for computational geometry.

Accepting: PhD and MCS students

Express your interest in working with Dr. Meng He.

Algorithms and Lower Bounds for Constructing Phylogenetic Networks

This project focuses on the mathematics, theory, and implementation of algorithms for studying the evolutionary relationships between sets of species. These relationships are captured in the form of distance measures between evolutionary trees or in the form of networks that represent the events that may have happened to produce a given set of species. Computational biologists use such comparisons in tools to study the emergence of antibiotic resistance in bacteria, tools to manage biodiversity or tracking the spread of diseases. The underlying algorithmic problems are often NP-hard but can be solved efficiently using approaches from fixed-parameter tractability.  This project focuses on developing fast algorithms for phylogenetic tree comparison and for exploring their limitations via lower bounds.

Accepting: PhD and MCS students

Express your interest in working with Dr. Norbert Zeh.

Algorithms and software tools for understanding microbial genomes and the microbiome

I am looking to recruit PhD students to work on any of several ongoing projects in my lab. Students with a CS background are welcome to apply, as are students with other expertise who meet the entry requirements for the Interdisciplinary PhD program. Projects include:

  • Using machine-learning and graph-based methods to predict which genes are present in microbiomes, and use this information to offset the uncertainties in experimental data
  • Development and implementation of new algorithms and tools to be integrated into our ARETE software (https://github.com/beiko-lab/arete) that identifies antimicrobial resistance genes and probes their history of transmission.

Other proposals that are relevant to my research program are welcome too.

Accepting: PhD students

Express your interest in working with Dr. Robert Beiko.

Cluster Banner 2

Continuous automated monitoring and prediction of the impact of climate change on ocean biodiversity

Tracking the location and presence of fish and marine mammals is necessary for both scientific study and conservation. The objective of this research is training integrated ML models with images, acoustics and text to answer questions about the diversity and abundance of life below water in monitored regions at scale. The successful applicant will apply contrastive learning, generative models, or other methods of learning with few or no labels. Goals include predicting climate change impacts on commercial fishes and marine mammals, regulatory monitoring of tidal energy, Carbon Dioxide Removal (CDR), and other ocean infrastructure, and modeling climate change mitigation efforts such as reducing fishing or introducing species to new areas.

Accepting: PhD students

Express your interest in working with Dr. Christopher Whidden.

Speech and language processing for the healthcare system

I am recruiting ambitious, experienced graduate students to work on deep learning in speech and natural language processing, brain data, machine learning in healthcare, and especially any intersection of these. One program of research has four components: 1) fundamental improvements and deeper understanding of state-of-the-art, transformer-based large language models, 2) modifying these models to process medical and healthcare texts for real-world tasks, 3) solving MLOps challenges in deployment, including scalability, generalizability, transparency, robustness, safety, and mitigation against bias, and 4) clinical implementation issues, including bioethics and usability. Our goals are to make major advancement in knowledge and in real-world impact.

Accepting: PhD and MCS students

Express your interest in working with Dr. Frank Rudzicz.

Interpreting Deep Learning Models of NLP

AI models, specifically deep learning models, have achieved state-of-the-art performance across a multitude of domains including computer vision and natural language processing (NLP). However, despite the benefits of deep learning models, their opaqueness is a major cause of concern. These models work as a black box and it can be impossible to understand how a model solves a task. This project touches various aspects of interpreting and explaining deep learning models, and involves applications enabled by interpretation such as domain adaptation, debiasing and style generation.

I am looking for students with a strong background in machine learning and deep learning, and exceptional problem solving and programming skills. Past experience with research and development projects involving deep learning, NLP or vision is a plus. 

Accepting: PhD and MCS students

Express your interest in working with Dr. Hassan Sajjad.

AI in Healthcare

My research area is "AI for Healthcare", focusing on both data-driven and knowledge-driven AI methods applied to a range of health related projects. I am looking for students to work in the general areas of (a) machine learning and deep learning for health data analytics, working with a wide variety of health data; and (b) semantic web and knowledge graphs for health knowledge management using clinical guidelines, clinical workflows and ontologies. The AI methods being developed target clinical decision support for outcome prediction, risk assessment, disease progression trending, hospital resource utilization and so on;  personalized healthcare; public health program; activity recognition for assisted living; clinical guideline computerization and execution, literature based knowledge discovery and environmental toxicology analysis related to chronic diseases. Our research is interdisciplinary where the projects are in collaboration with multiple medical specialities. 

Accepting: Phd and MCS students

Express your interest in working with Dr. Syed Abidi.

Copy of Cluster Banner 4

Spatial Analysis and Augmented Reality

In this project, we explore how spatial analysis techniques traditionally used in architecture and urban planning can support authoring, implementing, and evaluating building-scale immersive augmented reality experiences.   

The project involves iterative participatory design with content creators, software toolkit development in Unity 3D, spatial analysis using tools like QGIS, DepthMapX, R, and/or Matlab, designing and running "in-the-wild" field studies, and evaluating results using qualitative and quantitative approaches. In this project you will work in an interdisciplinary team that includes researchers in architecture, urban anthropology, and media studies.

Accepting: PhD and MCS students

Express your interest in working with Dr. Derek Reilly.

Craft Archive

The H.A.I.K.U. research group is all about harnessing the potential of hypertext to help individuals find and use information. Some of the projects are about fundamental issues but others are about doing something soon since technology and people co-develop so rapidly if you don't do something early you cannot have any influence, and there are many bad influences already. Find out more about available opportunities: https://haiku.cs.dal.ca/opportunities.html

Accepting: PhD and MCS students

Express your interest in working with Dr. Jamie Blustein.

VR/AR for Creative Tasks

VR and AR are becoming affordable and accessible for everyday use. One area that has become prevalent is using VR/AR for creative tasks, such as sketching in 3D. This new research project aims to create new adaptive and intelligent user interfaces for 3D design by identifying the actions and elements humans use when thinking spatially. The ideal candidate has a strong interest in VR and AR, HCI and user interface design.

Accepting: PhD students

Express your interest in working with Dr. Mayra Barrera Machuca.

MR Games 4 Change

Mixed Reality (MR) games for change are games that target pro-social collaborative behaviours. Within my research, I explore the creation of these games to promote socially driven education in public spaces for education and advocacy. 

Accepting: PhD students

Express your interest in working with Dr. Rina Wehbe.

Smarter Elevators

In collaboration with Solucore Inc. this project seeks to improve the experience of building management and administration as they manage elevators and lift systems within their buildings.

Accepting: PhD and MCS students

Express your interest in working with Dr. Rina Wehbe.

Making, Fab, Wearables

Maker culture and home fabrication allow for skill development and educational opportunities in Science, Technology, Engineering, Mathematics (STEM). This project explores game jam, hackathon, and maker culture with the aim of improving and making more accessible wearable computing.

Accepting: PhD students

Express your interest in working with Dr. Rina Wehbe.

Cluster Banner 4

Sustainable Software Engineering

The role of software development in sustainability is vastly understudied. Software profoundly affects all three pillars of sustainability: Environmental, Social and Economic. Inversely, the three sustainability pillars apply to every software project. The successful applicants will not only investigate the relationship between software engineering and sustainability but also develop and empirically evaluate tools or practices for improving software project sustainability.

Accepting: PhD and MCS students

Express your interest in working with Dr. Paul Ralph.

Hybrid Teams and the Future of Work

Most software companies are either considering a hybrid workforce strategy (employees work partly remotely, partly on-site) or have already adopted one. Refusing to accommodate remote work is crushing retention across the tech sector – the so-called “Great Resignation.” Indeed, hybrid work has many advantages for companies (e.g. lower overhead costs), employees (e.g. improved flexibility for parents and other caregivers) and society (e.g. improving workplace accessibility for people with disabilities). However, remote work undermines teams’ resilience, cohesion, and performance, and causes online-fatigue, poorly regulated workdays, loneliness, and coordination problems. The successful applicants will investigate how organizations can embrace a remote or hybrid workforce while overcoming challenges surrounding team cohesion, resilience, performance, and retention.

Accepting: PhD and MCS students

Express your interest in working with Dr. Paul Ralph.

Empirical Standards for Software Engineering Research

Peer review—the foundation of science—is ineffective, unreliable, prejudiced and opaque. It can only be fixed by transitioning to more structured review processes in which reviewers evaluate papers against specific acceptance criteria tailored to a paper’s individual research methodology (e.g. case study, controlled experiment). The successful applicants will create and evaluate tools to facilitate more structured review. The ideal candidate has good knowledge of web programming (e.g. HTML, CSS, Javascript) and an interest in research methods.

Accepting: PhD and MCS students

Express your interest in working with Dr. Paul Ralph.

Intelligent Network Repair

Networks have grown from small topologies connecting a dozen of devices to large, shared infrastructures supporting primary needs of our society. Today, we count on networked services for trading, commuting, monitoring weather conditions, meeting people. In order to provide reliable services, network operators need to cope with the daunting challenge of ensuring millions of flows from heterogeneous devices arrive at their destination on time and showing a reasonable throughput. Despite the significant advances recent Software Defined Networks (SDNs) provided towards managing large scale network infrastructures, they still fall short to enable fault-tolerant, performance-guaranteed data transmissions to the level next-generation applications such as 5G, smart cities, augmented reality and the Tactile Internet demand. In this project, we propose a new view to the problem of network reliability. Through Artificial Intelligence (AI) and Machine Learning (ML) techniques, we look for building a smart, highly scalable and robust network repair system. Our design will combine state-of-the-art machine learning techniques such as deep reinforcement learning and graph neural networks with high-performance and flexible network devices (e.g., P4 switches, NetFPGAs, and SmartNICs) to detect and correct network faults with high accuracy and in extremely short timescales. As a result, this project will promote the development of next-generation applications and their widespread adoption in production environments.

Accepting: PhD and MCS students

Express your interest in working with Dr. Israat Haque.

Research Data Infrastructure Software Maintenance and Evolution

Scientists are interested in sharing exciting research data collected using fun new technology (or, in some cases, are compelled to). Sharing FAIR data requires research data infrastructure (RDI: digital infrastructure organized to promote data sharing and consumption in support of research efforts). RDI software is often "homemade": created and deployed by people who have not received formal training in software engineering, or at organizations with primary mandates other than software development. These developers are often also users; they are adding features as they or their colleagues identify the need. Our understanding of software engineering as a field and practice does not universally translate to this software. This software is used by a growing set of users, but no one has systematically assessed its maintainability, longevity, technical debt, community resilience, or other indicators of long­term health, nor is it known if existing approaches to assessing these metrics are effective for RDI software. This research seeks to address that gap. 

Accepting: PhD and MCS students

Express your interest in working with Dr. Mike Smit

Explaining faulty software code with artificial intelligence

Software bugs are human-made errors in the code that prevent the code from working correctly. They cost the global economy billions of dollars every year and claim 50% of the development time. To correct a bug, one needs to detect its location within the code and understand its root cause. Over the last 50 years, there have been numerous attempts to automatically find and correct software bugs. However, only a little effort has been made to automatically explain the bugs, which is crucial to correcting any bug. This project will focus on designing tools and techniques to automatically explain the bugs or defects in the code. It will involve mining bug-fixing history from large-scale open-source projects, understanding code semantics through neural language modelling, and using advanced deep learning technologies (e.g., transformers, and code generation models).

Accepting: PhD and MCS students

Express your interest in working with Dr. Masud Rahman.

Cyber Security and Resilience

In this research project, we are going to work on monitoring and analysis of adversity and changes in the communication networks and services using machine learning and artificial intelligence approaches. I'm looking for interested students who are capable of independent as well as team based research on both wired and wireless networks, including the internet of things and vehicular networks.

Accepting: PhD and MCS students

Express your interest in working with Dr. Nur Zincir-Heywood.

Cross-chain Interoperability for Blockchain Smart Contracts

Cross-blockchain interoperability is one of the issues that is being actively investigated in both academia and industry. Most approaches address the cross-chain interoperability at the level of blockchain infrastructure and mechanisms have been proposed and implemented for cross-blockchain transfer of fungible and non-fungible assets. However, the cross-chain interoperability can also be supported at the level of smart contracts, which is the subject of this project. A smart contract is created that represents collaborative activities, such as transfer of messages, decision-making, and execution of atomic tasks, wherein the collaborative aspects are represented using Discrete Events - Hierarchical State Machines (DE-HSM) modelling, in which the concurrency is represented using DE and HSMs are used to represent functionality. Thus, the collaborative activities are agnostic to the blockchain and "only" the individual tasks need to be represented using the blockchain's native scripting language. To achieve consensus for execution of an individual task, the task is executed by each of the collaborators (executing on a different blockchain) and then attestation approach is used to achieve the consensus on the results of such a task execution.  

Accepting: PhD students

Express your interest in working with Dr. Peter Bodorik.

Security for Healthcare Internet of Things

The primary objective of this project is to investigate vulnerabilities, security threats and intrusions on Healthcare IoT systems, and design intrusion detection and prevention mechanisms to mitigate cyber-attacks on such systems. The project will explore an integration of machine learning approaches with biometric parameters to ensure confidentiality, integrity and authentication of healthcare data, and experimentally validate these mechanisms on a Healthcare IoT test bench.

Accepting: PhD and MCS students

Express your interest in working with Dr. Srini Sampalli.

Detection, prediction, and prevention of cyber-attacks on critical infrastructure

The objective of this project is to investigate, design and implement mechanisms for the detection, prediction, and prevention of cyber-attacks such as Distributed Denial of Service (DDoS) on Supervisory Control and Data Acquisition (SCADA) systems, which form the core of critical infrastructure such as power grids, water supply control, and nuclear systems. These strategies will be developed using machine learning classifiers with extension to deep learning techniques. The project also aims to explore the integration of distributed systems, fuzzy logic and machine learning approaches to predict and detect Distributed Denial of Service (DDoS) attacks with higher detection accuracy and faster detection time.

Accepting: PhD and MCS students

Express your interest in working with Dr. Srini Sampalli.

Intelligent Internet of Things for Healthcare

The project entails the design of secure and reliable assistive wireless technologies for healthcare. Past work in this area by my students has led to the innovation of touchscreen-based Braille keyboards for the visually impaired, applications using wireless technologies in the areas of medication error detection and prevention, and a tool for monitoring of pregnant women for signs of premature labour. The work by my Masters student Steve Dafer's work has resulted in an IoT-based tool called EMPWRD (Enhanced Modular Platform for People with Rigid Disabilities) that uses artificial intelligence and natural language processing techniques integrated with IoT. This tool enables paralyzed patients to control devices and even make live phone calls. I am looking for students to extend work in this area.

Accepting: PhD and MCS students

Express your interest in working with Dr. Srini Sampalli.

Learning-based Resource Management for Internet of Vehicles

I currently have a couple of open positions for highly self-motivated MCS, PhD students interested in wireless networking and artificial intelligence (AI). My current research project focuses on supporting real-time applications in high-confidence Internet of Vehicles (IoV). Traditional analysis tools/algorithms are unable to cope with the full complexity of IoV or adequately predict system behavior due to great challenges that arise from the high mobility, dynamic changing environment and intrinsic heterogeneity of such systems. Therefore, the goal of my research project is to reap the benefits of AI to address aforementioned challenges in IoV. 

Accepting: PhD and MCS students

Express your interest in working with Dr. Yujie Tang.