DND/NSERC Research Trainee Matching Program opportunities

Defence Research and Development Canada (DRDC) has partnered with the Natural Sciences and Engineering Research Council of Canada (NSERC) to provide opportunities at DRDC labs across Canada.

NSERC Researcher Matching Program candidates can apply to work on the following ongoing projects. The catalogue will be updated yearly.

For more information on eligibility and application requirements, please refer to the application instructions.

On this page

• Atlantic Research Centre
  • Machine learning
  • Interaction between human operators and autonomous systems
  • Artificial intelligence (AI)
  • Fracture toughness of naval steels
  • Advanced underwater stealth technologies
  • High power density energy storage in cold climates (-40C)
  • Advanced polymer materials
  • Geothermal energy potential in the Arctic
  • Shipboard fire propagation modelling and simulation
• Centre for Operational Research and Analysis
  • Mathematical and computational modelling of the supply and logistics chains
  • Optimization of training schedules and resource allocation
  • Optimal control of military population dynamics
  • Mathematical modelling and computer simulations representing air defence systems and operations
• Ottawa Research Centre
  • Cyber operations and resilient communications (CORC)
  • Navigation and communications electronic warfare (NAVCOM EW); assured precision, navigation and timing
  • Navigation and communications electronic warfare (NAVCOM EW); communications electronic warfare
  • Radar electronic warfare (REW)
  • Radar sensing and exploitation (RSE)
  • Space and information, surveillance and reconnaissance (ISR) applications
• Suffield Research Centre
  • Electrophysiology / traumatic brain injury (TBI)
• Toronto Research Centre
  • Intelligence analysis for national defence and security
  • Human systems effectiveness
  • Translational biomedical research in military trauma, deployment health, combat injuries and mental disorders

Atlantic Research Centre

Machine learning

Underwater warfare section

Deep learning has become a core technique for automatically recognizing a variety of threat targets in data obtained from underwater sensors. However, state-of-the-art deep learning models are designed for tasks such as computer vision and natural language processing and may not be well suited to the challenging underwater environment. One of the main challenges in applying deep learning to this domain is the difficulty in achieving efficient domain transfer since underwater data is distinct from natural images. For example, a model capable of detecting thousands of different classes of objects from the red, green, and blue (RGB) channels of natural images may be overly complex for detecting objects in single channel side looking sonar data. In addition, large deep learning models have significant computational performance requirements that may limit their utility on board remote underwater sensing platforms. Ongoing projects include the use of custom neural networks for diverse underwater automatic object recognition tasks using data obtained from side-looking sonar, wideband sonar scattering, passive acoustic sensors, and active sonar. Challenges include efficient neural network design and working with small or otherwise limited training datasets.

Interaction between human operators and autonomous systems

Maritime systems experimentation and analytics (M-SEA) section

The maritime systems experimentation and analytics (M-SEA) section of DRDC Atlantic Research Centre (ARC) is actively engaged in an ongoing autonomous systems project. The primary objective of this project is to identify and address human factor concerns within future human-autonomy teams. The overarching goal is to establish smooth collaboration and interaction between human operators and autonomous systems, thereby maximizing the efficiency of their joint contributions to naval operations.

The project's current focus areas encompass several key aspects. Efforts are being directed toward enhancing trust and confidence in human-autonomy teams. Additionally, the project is dedicated to pinpointing the necessary prerequisites for advancing collaboration between humans and autonomous systems. Furthermore, the team is involved in the design and testing of upcoming concepts of operation for human-autonomy partnerships, specifically within naval contexts.

Artificial intelligence (AI)

Maritime systems experimentation and analytics (M-SEA) section

DRDC Atlantic Research Centre is looking for a post doctorate fellow to assist in researching how artificial intelligent (AI) agents can be exploited in the information domain. With an aim to manage, coordinate, and exploit information related to the physical and digital environment, a group within DRDC is developing a prototype of an information domain system to experiment with the assessment of North American defence and security data sources in an integrated manner.

This prototype system will build its own situational awareness (SA) of the information domain and initiate AI responses based on this SA, to either improve the SA of the system or improve the SA of the user. While certain responses will be straightforward, others will benefit from an AI agent approach to decision making.

For this project, the candidate will explore AI research and development related to concepts that help construct system situational awareness. This will be applied research in the field of AI, focused on the automated assessment of the system and modifications to aspects of data collection, distribution, processing, etc. for improved system performance.

As part of a small team with varied research specialties, the candidate will perform independent and collaborative scientific research. The candidate will also be expected to communicate scientific results to peers, to members of the Canadian Armed Forces (CAF), at conferences, and in writing, both internally and for public journals.

A strong background in AI agent research is required. Creativity and an ability to translate/transform concepts from various scientific fields into the information domain would be advantageous.

Fracture toughness of naval steels

Advanced materials and engineering section

Fracture and failure of naval steels is a topic of enduring interest and relevance to DRDC and the Royal Canadian Navy (RCN). Prevention of fracture is important in many maritime, and other, industries, but the requirements are uniquely high for naval vessels. They have larger crews, less redundancy in design, and are more likely to sail in rough seas, at high speeds, and carry out aggressive maneuvers. Naval vessels may be subject to explosions and impacts. They must operate in all oceans and in a wide range of environmental conditions. In short, they have unique and vitally important survivability requirements. For parts and structures that simply must not fail by catastrophic fracture, toughness is the limiting material property. Material toughness is not well understood and is difficult to assess. Whether a crack will form and grow in a material depends on more than the material properties; it also depends on the type of loading and the geometry of the part. This program of work is designed to characterize the toughness of naval steels. Fracture and failure will be investigated in a variety of naval steels, in a variety of conditions, over a range of strain rates, all relevant to materials and conditions of use by the RCN, present and future.

Advanced underwater stealth technologies

Advanced materials and engineering section

Underwater acoustic coatings are a key stealth technology for reducing the susceptibility of naval vessel detection by active or passive sonar. This project aims to investigate the acoustic performance of modern design architectures, such as those covered under the class of acoustic metamaterials, for use as coatings on future platforms. Depending on expertise, areas of development include characterizing dynamic material properties (e.g. dynamic mechanical analysis), electronic and mechanical system design for novel dynamic bulk modulus apparatus (DBMA), implementing systems to measure vibration decoupling performance, improving experimental acoustic tank analysis code (MATLAB), and numerical modelling of acoustic metamaterials (COMSOL).

High power density energy storage in cold climates (-40C)

Advanced materials and engineering section

Next generation energy storage is required to operate certain Canadian Armed Forces (CAF) equipment from portable/wearable to mobility platforms in cold climates. For example, current batteries/capacitors cannot function in cold temperatures and research into new/optimized materials to power pack systems will be needed to meet future power requirements.

Advanced polymer materials

Advanced materials and engineering section

Polymer materials are utilized across the Canadian Armed Forces (CAF) in applications ranging from protective coatings to wound dressings. Research in polymer materials in the advanced materials and engineering section at DRDC Atlantic Research Centre is focused on leveraging fundamental polymer science to develop and improve materials used by the Canadian Armed Forces (CAF). Specific examples of ongoing research projects include developing new adhesion promoters for protective coatings on navy vessels, the interaction of shockwaves polymers, and materials for flexible electronics.

Geothermal energy potential in the Arctic

Advanced materials and engineering section

The Department of National Defence and the Canadian Armed Forces (DND/CAF) strategic Arctic infrastructure as well as off-grid Arctic communities rely heavily on fossil fuels for heating and electrical energy needs. The advantages of stable, consistent, resilient, and renewable energy with an extremely low carbon footprint makes geothermal energy a viable renewable energy option for the Arctic region. Several challenges such as high exploration and development costs and limited data for evaluating resources in remote regions must be overcome. Thus, a first-order feasibility assessment of the geothermal energy potential in the region and at specific DND/CAF strategic locations is being conducted.

Shipboard fire propagation modelling and simulation

Advanced materials and engineering section

Fire can be a catastrophic event aboard a ship. This is even more so a problem on warships, which are more prone to entering dangerous situations than commercial ships. In order to protect Canada in our home waters and prevent conflict by promoting global stability through deployments overseas, the Royal Canadian Navy (RCN) will require a fleet of sufficient size to operate in its three oceans and to deploy abroad on an ongoing basis. As such, they cannot lose ships to fire, hence fast, efficient firefighting is a must. This will involve the use of novel fire-resistant materials, new firefighting technologies, and an in-depth understanding of fire development and movement, developed through modelling and simulation. Many industries use fire and smoke modelling and simulation (M&S) (e.g., insurance, nuclear) but the maritime environment presents some unique challenges that require further development and validation of the tools. Unlike an office building, warships contain all the aspects of a small town in one confined area: heavy industrial (engine rooms), bulk flammable storage, warehousing, accommodations, galleys, and offices. When modelling the fire, one must consider that battle damage can affect the water pressure in the fire mains, the sprinkler nozzles, and hatches can be jammed shut (or open). Ships have complex ventilation and exhaust systems that need to be considered. Ship motion can affect naval damage control. Fuel loads and firefighting water pools can move depending on ship motion. The addition of pools of firefighting water can affect damage stability. The aim of the research project is to develop a suite of validated modelling tools for understanding the potential progression of fires and the ensuing smoke/toxic gas movement and distribution through a naval platform. The modelling tools will provide a predictive capability to be used to evaluate new ship designs or design modifications within existing platforms against fire scenarios. They may also be used to evaluate changes to firefighting technologies and doctrine or support innovation and technological development without investing in untried technologies. The outcome of this research is a new defence capability in numerical modelling of fire scenarios that can be used by the RCN to provide improved survivability assessments of their platforms.

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Centre for Operational Research and Analysis

Mathematical and computational modelling of the supply and logistics chains

The resiliency of supply and logistics chains is an important component of credible defence capabilities. The present sustainment chains are extremely complex, spanning a variety of companies, and often distributed across several countries. Furthermore, during crises, adversaries could disrupt these chains, and in many cases there are also internal vulnerabilities (labour disputes, lack of supplies, problems with the delivery chains, climate change-related problems – i.e., forest fires can interrupt transit links for supplies).

Canadian Army operational research team is currently conducting a large study, in collaboration with University of Victoria, and Canadian Joint Operations Command exploring diverse aspects of this problem, including mathematical and computational modelling of the supply and logistics chains, multi-criteria decision analysis, vulnerabilities to hybrid threats, and sustainment requirements for a major crisis or conflict. The postdoctoral fellow would work with a team of defence scientists, military, and academics on one or more subproblems. The details could be determined based on the postdoc’s interest and competencies.

Optimization of training schedules and resource allocation

Training in certain military trades involves considerable time and resource investment. One prominent example of this is pilot training, specifically the demanding training for fighter pilots. The full training curriculum in these trades takes many years to complete and consists of numerous training phases and courses, each broken down into numerous individual training activities. The resources required to complete each training activity are both high value and scarce, such as full-motion simulators, properly maintained aircraft, qualified instructors, and runways. This is further complicated by resource requirements that are not rigid, (e.g., a particular training requirement may be met either in a simulator or in an aircraft sortie). Training activities are further constrained by the need to group certain numbers of students and instructors together, regulations (such as maximum flying hours per pilot and per fleet) and appropriate weather conditions. The training curriculum, while generally sequential, also includes courses and activities that need not be completed in a specified order, and each training course is typically made available on a particular schedule and accepts a certain minimum and maximum number of students at a time. This is a system in which poor planning can result in significant negative consequences: students may experience excessive delays while waiting for their next training activity; additional training resources may need to be procured at significant cost to accommodate unanticipated surges in training demand, and overall training throughput may lag behind that needed to sustain the operational capabilities of the Canadian Armed Forces (CAF), disrupting Canada’s ability to respond and contribute to domestic and international deployments. There is a need to be able to model and optimize these complex training systems, with the broad objectives of minimizing training delays, total training time, and resource requirements. This involves both optimizing the scheduling of training activities and the allocation of limited resources to concurrent training demands. A body of work already exists in this area, including models and optimization approaches, and the first task would be to become familiar with this work to understand prior efforts and directions that are novel. The desired outcome of the work is to develop optimization solutions that can accommodate the real-world complexities and constraints of the CAF’s training systems, and that will produce practical benefits including maximizing student throughput given existing resource constraints, determining the minimum resource pools (and hence cost) that will meet student graduation requirements, and the ability to adapt the models to changes such as transitions to new equipment (such as new aircraft, and new weapon systems), and surges in training demand required to develop new military capabilities.

Optimal control of military population dynamics

Military professional development takes the form of a systematic progression where personnel go through various phases: they enter the system as new recruits and receive training through schoolhouses and mentorship before becoming fully productive members of the workforce, and then eventually releasing from the military. To reduce personnel costs while ensuring a necessary workforce size and readiness to respond to domestic and international obligations, careful planning must be undertaken. To assist in this task, DRDC is in the process of developing a large cradle-to-grave simulation to model the system dynamic of the military workforce.

How to optimally control such a system to reach goals set by decision makers (e.g., growing population levels by a specified time) remains an open problem. Principal amongst the challenges is the sheer size of the state space (tens of thousands of individuals), and the time horizon and resolution over which the planning is sought (decades and days). In all but the simplest cases, exact optimal approaches will lead to prohibitive computational time which severely limits their practical use. These challenges are further compounded by the fact that the size of the state space and its underlying system dynamics may be only partially knowable.

Given these challenges, the purpose of this project is to develop system-agnostic approaches, otherwise referred to as model-free or data driven, to control the population dynamics based on observations and measurements of the system's evolution, as a proxy for the system's dynamics. Reinforcement learning is the natural theoretical and computational framework for developing model-free adaptive optimal control schemes, where the optimality is encoded into a user defined reward, which is the criterion to evaluate the quality of control actions imparted on the system. Control actions are then modified to increase the reward, defining an adaptive scheme coupled with the system's dynamics. The reinforcement learning paradigm for data driven optimality allows for embedding state and reward constraints, and to consider finite time horizons with target values for the corresponding states.

Mathematical modelling and computer simulations representing air defence systems and operations

A strategic missile defense system must be robust enough to be able to cope with a variety of threats ranging from traditional ballistic, subsonic, and low-supersonic low flying missiles, to hypersonic glide vehicles, cruise missiles, and drones. In essence, there are four elements to a missile defense system: detection, identification, interception and destruction of the missile threats. The integrated system effectiveness is a compound of the effectiveness of each element, which consist of many sub elements.

We seek development of mathematical models and computer simulations representing air defence systems and operations. We are interested in developing models that can assess effectiveness of the air defence system in responding to unexpected events. For example:

• the number of threats may be different from intelligence information or there could be decoys;
• the trajectory of the threat can be non ballistic such as a hypersonic missile threat;
• the launch location of the threat can also be unforeseen;
• the destruction of the threat can be incomplete (i.e., the debris of the threat can still be dangerous).

The postdoc will be working with a team of scientists supporting NORAD air defence analysis.

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Ottawa Research Centre

Cyber operations and resilient communications (CORC)

The cyber operations and resilient communications (CORC) section at DRDC Ottawa conducts cyber defence research to protect computer and communication networks for the Canadian Armed Forces and conducts communications research to enhance and enable mission-critical communication systems. Current projects include:

• Research into automating cyber attack and defence using state-of-the-art reinforcement learning methods to train autonomous red and blue agents. A key component of this work is the ongoing development of a self-contained cyber range with red and blue tools to allow AI agents to train individually and competitively, both on local machines and in the cloud. Another supporting area of interest is the application of large-scale natural language processing models to cyber defence problems.
• Research into defence applications of 5G mobile technology and Internet of Things (IoT) capabilities, which offer the opportunity to use more sensors tactically, thereby increasing situational awareness. Questions arise surrounding how to securely deploy 5G/IoT for military communications. This work involves studying, modelling, simulating and analyzing these communication networks in defence scenarios, identifying vulnerabilities and implementing mitigation algorithms.
• Research to develop new technical metrics and key performance parameters for cyber offensive and defensive operations, including quantifying the damages from a potential attack on the target network or system.
• Research into the use of low earth orbit satellites to enhance and enable low-latency, high-bandwidth ubiquitous communication, especially in remote or inaccessible locations; this research focuses on the development of technologies enabling a resilient space architecture integrating communications, sensing and command and control.

Navigation and communications electronic warfare (NAVCOM EW); assured precision, navigation and timing

The navigation and communications electronic warfare (NAVCOM EW) section at DRDC Ottawa Research Centre is responsible for advanced research in assured precision, navigation, and timing (aPNT) for the Department of National Defence/Canadian Armed Forces (DND/CAF). One overarching mission within this section is to assure positioning using alternatives to the global navigation satellite system (GNSS). Alternative sensing modalities, sensors, and processing solutions are being sought that augment our current technologies and are more accurate, unjammable, ubiquitous, and available where GNSS is unsuitable. Current projects include:

• fibre-optic gyroscope
• nitrogen vacancy diamond magnetometer/gradiometer
• gravity gradiometer
• optical atomic clock
• localization algorithms and simultaneous localization and mapping (SLAM)

Navigation and communications electronic warfare (NAVCOM EW); communications electronic warfare

The navigation and communications electronic warfare (NAVCOM EW) section at DRDC Ottawa Research Centre conducts research and development to support the Canadian Armed Forces (CAF) in detection, identification, geolocation and countermeasures for communications signals, and provides force protection (FP) against radio controlled improvised explosive devices. Current projects include:

• Research and development on antennas with a focus on modelling and simulation of arrays and array signal processing. A key component of the antenna research is to achieve higher gain, path diversity (multiple-input and multiple-output), communications reliability, and radio detection finding. The antenna research supports the activities within the section for counter drone, communications electronic support and FP.
• Research study on security and vulnerability on 5G physical layer signalling. The research involves modelling, simulating, and analyzing the physical layer of communication networks to identify the vulnerabilities and potentially implementing mitigation algorithms.
• Research on counter drone radio frequency (RF) sensing. The sensing of a drone is to detect, identify and track in real-time. The research and analysis of radio frequency (RF) sensing techniques such as wide-area sensing, distributed sensing and multi-layered sensing provides key performance parameters for detection and identification of drones.
• Research on RF sensing topics for communications electronic support  activities. The focus would be to utilize intelligence through a range of RF surveillance and collection devices, giving the decision makers timely insight for use with electronic protection, electronic attack, or other electronic warfare systems. The areas of interest are low probability of intercept and low probability of detection signals, application of AI, target identification, signal detection, and classification and emitter geolocation.

Radar electronic warfare (REW)

The radar electronic warfare (REW) section at DRDC Ottawa conducts research to improve the capability of the Canadian Armed Forces to:

• Detect, identify, and geolocate all radar emitters present in a tactical operating environment in order to provide own and allied military commanders with accurate situational awareness information.
• Fuse information from passive radar sensor with information from other sensors in order to improve the accuracy and quality of situational awareness information.
• Develop electronic attack techniques to:
  • Inhibit the adversary radar’s ability to detect and exploit own and allied radar emissions and thereby degrade the adversary’s situational awareness capabilities.
  • Suppress the adversary radar’s ability to track, target, and guide weapons onto own and allied platforms, and thereby improve platform survivability.

Current projects include:

• Specific emitter identification (SEI) is the capability to identify individual instances of the same model of radar by exploiting unique, subtle characteristics in the transmitted waveform that vary between instances. There is an ongoing need to improve the discrimination performance of SEI algorithms.
• There is a need to develop algorithms that will continually re-optimize electronic attack techniques in a real-time tactical operating environment (i.e., perform electronic battle damage assessment), in order to ensure that a desired effect is achieved (e.g., to perform counter-targeting and counter-surveillance against an adversary’s radar; to effectively jam an adversary’s parameter-agile weapon guidance radar) despite the relative motion of the various platforms involved and actions the adversary radar may take (i.e., electronic protection) to mitigate these effects.
• There is need to accurately direction-find and geolocate radar emitters under a variety of operating scenarios using a variety of equipment. For example, one or more electronic support receivers may be involved, and some of these may be in motion. The radar itself may be in motion or stationary, and transmit a variety of agile waveforms (e.g., various pulse widths; various intrapulse modulations; frequency-agile; pulse repetition interval (PRI)-agile; low peak power but high duty cycle).
• There is a need to accurately deinterleave radar pulses in a congested radio frequency (RF) environment in real-time. That is, the pulses must be sorted according to the radar that transmitted them. This accuracy is important to improve the accuracy of downstream signal processing techniques that will be used to identify and geolocate each radar. Deinterleaving is challenging because many radars are parameter-agile, especially with regard to pulse frequency and pulse repetition interval. Therefore, pulses from multiple radars may overlap in time and/or frequency.
• The tactical RF operating environment can be congested. It may contain a variety of radar emitters, variously operated by allies, neutrals, and adversaries, performing various functions that may be threatening or not (e.g., surveillance versus target tracking). It may also contain non-radar emissions (e.g., communications). All these emissions need to be identified and prioritized for response (e.g., devote the majority of resources to jamming the adversary’s weapon guidance radar, and fewer resources to jamming their surveillance radar, but do not jam the allied target tracking radar). Artificial intelligence (AI) and machine learning (ML) techniques could possibly be used to optimize the use of allied resources to respond in congested and contested environments. This could be done through a combination of off-line training and on-line refinement. These concepts and algorithms need to be explored in greater detail.
  The use of multiple (possibly a “swarm”) of drones and/or uncrewed surface vehicles (USVs) (collectively referred to as “UxVs”) make it possible to deliver electronic attack (EA) techniques that are more effective compared to those that are delivered by a single platform. It is therefore necessary to develop concepts describing how such coordinated and distributed EA techniques can be employed for defensive and offensive purposes, and to develop algorithms that will devise and deliver these techniques, as well as optimize their performance in real-time.
• In order to perform EA effectively against an adversary’s radar, it is useful to know the time and frequency when the next pulse will be transmitted.  This has become difficult now that many radars are parameter-agile. The development of an accurate pulse prediction algorithm will improve EA effectiveness by allowing EA systems to allocate resources (typically transmitted power and bandwidth) more appropriately.

Radar sensing and exploitation (RSE)

The radar sensing and exploitation (RSE) section at DRDC Ottawa Research Centre is responsible for advanced research in radar systems and radar signal processing for DND/CAF. The mission of the RSE section is to improve the performance and viability of radar technologies and concepts. The RSE section is leading 13 research projects across six lines of effort:

• quantum radar
• radar signature management
• tactical radar (airborne radar and ground-based radar)
• naval radar
• over-the-horizon radar
• space-based radar

Space and information, surveillance and reconnaissance (ISR) applications

Current projects in the space and information, surveillance, and reconnaissance (ISR) applications section at DRDC Ottawa Research Centre include:

• Continental defence modelling: investigate system-of-system solutions to provide science-based advice on NORAD modernization and continental defence by advancing technologies and concepts related to sensor network extension and integration, through modelling and simulation as well as in situ demonstrations and experimentation. Research is focused on characterizing air and hypersonic threats for modelling and simulation purposes, understanding how different sensors may be integrated to optimize coverage, and determining how space-based assets may be used to detect and track potential targets (maritime, air, hypersonic).
• Space domain awareness (SDA) and operations – develop space missions and concepts for on-orbit experimentation to ensure space-based assets are defended and protected. Research includes use of on-orbit optical sensors for in-situ orbital measurements and development of the Canadian astronomy capability, orbit determination (passive radio frequency [RF] and radar), surveillance of space in geosynchronous earth orbit, optical space object characterization, and orbital prediction products. Additional exploration into how to adapt existing star trackers and meteor cameras for unique and opportunistic data collection for space surveillance is of interest, along with proximity sensing technologies and requirements.
• Space radio frequency intelligence, surveillance, and reconnaissance – conduct research and development and analysis to enhance surveillance from space capabilities for DND/CAF. Enhance the DND/CAF space common operating picture through multi-sensor integration, data fusion, and machine learning. Create a suite of tools and frameworks that enable integrated execution of the full tasking, collection, processing, exploitation, and dissemination (TCPED) cycle for advanced space-based sensors. Conduct experiments with allies and partners with on-orbit sensors and move toward a hybrid space architecture.

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Suffield Research Centre

Electrophysiology / traumatic brain injury (TBI)

Casualty management section (CMS)

Research at DRDC Suffield Research Centre aims to investigate mechanisms of and therapeutics for traumatic brain injury (TBI) caused by blast and/or concussions. Specifically, we use behavioral, biochemical, confocal, and electrophysiological approaches to study molecular and cellular mechanisms of TBI. A post-doctoral fellow position is available to use the existing electrophysiology/patch clamp facility to study mechanisms of TBI.

We seek candidates who are interested in decoding the molecular/cellular causes of brain injury induced by blast, concussion or organophosphorus nerve agents. The successful candidate will join an interdisciplinary team at DRDC Suffield. Individuals with excellent past record of research achievement in electrophysiology, in particular synaptic studies, are encouraged to apply. The candidate is expected to perform in vitro electrophysiological studies (both field and patch clamp) using acute brain slices and possibly primary neuronal cultures. Excellent technical optimization and problem-solving skills will be required.

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Toronto Research Centre

Intelligence analysis for national defence and security

Intelligence, influence, and collaboration section

Successful candidates will work with a team of researchers, led by Dr. David Mandel, on applied cognitive science in support of intelligence (i.e., intelligence analysis for national defence and security). The position would involve theory development, hypothesis generation, hypotheses testing, analyzing research data, and sharing findings in publications and technical reports. Recently examined topics include:

• Methods for optimally recalibrating and aggregating forecasts and probabilistic assessments (i.e., exploiting the "wisdom of crowds").
• Methods for communicating uncertainty effectively and documenting limitations with existing verbal uncertainty approaches.
• The effectiveness of structured analytic techniques used to debias analysts' judgments and testing alternative approaches that leverage insights from cognitive psychology and decision science.
• How AI (e.g., large language models such as ChatGPT) might influence the production and consumption of anticipatory intelligence over the next several years (i.e., by examining how decision-makers respond to human vs. AI intelligence and advice).

Human systems effectiveness

Human effectiveness section (HES)

Various projects are available in the areas of human-autonomy teaming, learning and training, enhanced vision, and brain health. Position(s) would involve theory, concept, model and prototype development, hypotheses testing, research data analysis, and sharing findings in publications and technical reports. Scholarship recipients will work with senior defence scientists in the relevant focus area, with oversight by Dr. Philip Farrell.

The mission of the human effectiveness section (HES) is to provide scientific advice on the operational performance of Canadian Armed Forces (CAF) members and organizations by optimizing and augmenting human-technology interaction. This is done through human systems engineering, learning and training, enhanced vision, and brain health considerations. To achieve this aim, the section’s core capability is in human effectiveness as enabled by psychology, engineering, and human factors. This includes expertise in military intelligence and situational awareness systems as they relate to cognitive, organizational, and operational effectiveness, and the human dimension of command and control. Laboratory experiments, field studies, international collaboration and contracting are some of the ways HES generates knowledge to provide expert advice to the CAF. As knowledge of human behaviour and technology development continue to advance, HES will look to grow its capability in the areas of human-autonomy teaming, human interaction with virtual and mixed reality, human interaction with mobile computing, simulation-based training, and the use of cognitive modelling or artificial intelligence to understand and enhance human effectiveness, especially in complex sociotechnical systems.

Translational biomedical research in military trauma, deployment health, combat injuries and mental disorders

Operational health and performance section

Working with a team of researchers including Dr. Shawn Rhind, Dr. Henry Peng, and Dr. Jing Zhang, the post-doctoral fellow in translational biomedical research will develop and conduct highly creative, ground-breaking computational and laboratory work, and support clinical research studies (i.e., “bench-to-bedside-to-battlefield”). The work is directed toward the identification of cellular and molecular biomarkers of high relevance to military trauma and deployment health, as well as personalized medicine for combat injuries and mental disorders. This post-doctoral fellow will work with a multidisciplinary team of defence scientists and technologists, with expertise ranging from state-of-the-art AI data science to laboratory and clinical biomedicine. Specifically, they will contribute to ongoing DRDC projects on AI-assisted algorithms for blood transfusion and the resuscitation of trauma patients experiencing hemorrhagic shock, which would enhance both dry and wet lab capabilities for military medicine. The incumbent will be given the opportunity and guidance to research and develop machine learning models for personalized blood transfusion and novel hemostatic agents for battlefield resuscitation.

In collaboration with the Canadian Forces Health Services and local university hospitals, DRDC Toronto Research Centre (TRC) has carried out cutting-edge translational research for military medicine. Using both the latest computational and lab technologies (machine learning, global and specific blood testing, high-throughput screening and analysis) and clinical trials (retrospective, prospective and randomized design) TRC has made progress in the research areas of hemorrhage control and fluid resuscitation, early diagnosis and treatment of mild traumatic brain injury, and post-traumatic stress disorder. Specifically, we are developing AI-enabled transfusion algorithms for personalized trauma care, conducting lab and clinical studies of blood products (dried blood components and whole blood) for remote damage control resuscitation and novel hemostatic biomaterials (particles and dressings) for stopping non-compressible haemorrhage at the point of injury, as well as investigating biomolecular basis and therapeutic interventions for military operational stress injuries and (psycho)physiological trauma.

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